MCP GitLab Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation3/5
The tool set has clear distinct purposes for core GitLab operations like issues, MRs, commits, and projects, with good descriptions. However, there is significant overlap in user-focused tools (e.g., gitlab_get_user_details, gitlab_get_user_contributions_summary, gitlab_get_user_activity_feed) that could confuse agents about which to use for specific user analysis tasks, as they retrieve similar data with subtle variations in scope and detail.
Naming Consistency5/5
Tool names follow a highly consistent snake_case pattern with a clear 'gitlab_' prefix and descriptive verb_noun combinations (e.g., gitlab_list_issues, gitlab_create_commit, gitlab_get_user_details). This predictability makes it easy for agents to understand and navigate the tool set without confusion from mixed conventions.
Tool Count2/5
With 72 tools, the count is excessively high for a GitLab server, creating cognitive overload. Many tools are highly specialized (e.g., multiple user analytics tools like gitlab_get_user_code_changes_summary, gitlab_get_user_resolved_threads) that could be consolidated into fewer, more general-purpose tools without losing functionality, making the surface feel bloated and difficult to manage.
Completeness5/5
The tool set provides comprehensive coverage of GitLab's domain, including full CRUD operations for issues, MRs, commits, snippets, and projects, along with advanced features like batch operations, diffs, searches, and user analytics. There are no obvious gaps; agents can perform end-to-end workflows from code changes to deployment and user management without dead ends.
Average 4.2/5 across 72 of 72 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes what the tool returns (e.g., 'MR comment information' with details like content, type, MR context) and mentions pagination via 'per_page' and 'page' parameters. However, it lacks details on behavioral traits such as rate limits, authentication needs, error handling, or whether it's a read-only operation (though implied by 'Get'). This leaves gaps in transparency for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections: purpose, returns, use cases, parameters, and an example. It is appropriately sized and front-loaded with the core purpose. However, the 'Returns' section is somewhat verbose with bullet points that could be condensed, and the 'Use cases' might be redundant if the purpose is already clear, slightly reducing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description provides good context on what the tool does and its parameters. However, it lacks details on output format (only described in bullet points without schema), error conditions, or performance implications (e.g., pagination limits). For a tool with 6 parameters and complex filtering, this leaves some gaps in completeness for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents parameters well. The description adds value by listing all parameters with brief semantics (e.g., 'Filter by type (review, discussion, all)') and provides an example that clarifies usage. It compensates for the schema's lack of enums by explaining options like 'comment_type' and 'mr_state,' enhancing parameter understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get all comments authored by a user on merge requests' with additional context about including 'code review discussions.' It specifies the resource (comments on merge requests) and action (get/find). However, it does not explicitly differentiate from sibling tools like 'gitlab_get_user_issue_comments' or 'gitlab_get_merge_request_notes,' which reduces clarity in distinguishing usage scenarios.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides 'Use cases' (e.g., 'Code review participation tracking') which imply when to use this tool, but it does not explicitly state when not to use it or name alternatives among sibling tools. For example, it doesn't clarify if this should be used instead of 'gitlab_get_merge_request_notes' for user-specific comments. This leaves usage context somewhat implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the return format ('All branches with latest commit info') and provides an example response, which is helpful. However, it doesn't mention important behavioral aspects like whether this is a paginated endpoint, rate limits, authentication requirements, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, returns, usage, optional feature, example, related tools). While the example response is detailed, it serves a useful purpose in clarifying the output format. The related tools section is helpful but could be more concise by focusing only on the most relevant siblings.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description provides reasonable coverage of purpose and usage. The example response partially compensates for the lack of output schema. However, important contextual information about authentication, pagination, error handling, and the relationship to other list tools in the extensive sibling set is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with one parameter documented in the schema. The description mentions 'Optional: Search filter' which doesn't directly correspond to the 'project_id' parameter in the schema, creating some confusion. It adds minimal value beyond what the schema already provides about the single parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('repository branches'), making the purpose immediately understandable. It distinguishes from some siblings like 'gitlab_create_branch' and 'gitlab_delete_branch' by indicating a read operation, though it doesn't explicitly differentiate from other list tools like 'gitlab_list_tags' or 'gitlab_list_commits' in the same repository context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'Use when' section provides clear context ('Checking branches, finding feature branches'), which helps the agent understand appropriate scenarios. It mentions an optional search filter, though it doesn't explicitly state when NOT to use this tool or name specific alternatives among the many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'Filtering: By action type, target type, date range' and provides 'Example activities' which give context about what types of events are returned. However, it doesn't disclose important behavioral traits like whether this is a read-only operation, rate limits, authentication requirements, or pagination behavior (though pagination parameters exist in the schema). The description adds some value but leaves gaps for a tool with 7 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections ('Returns:', 'Use when:', 'Filtering:', 'Example activities:', 'Related tools:') and uses bullet points effectively. It's appropriately sized at 7 sentences/lines, with each section adding value. However, the 'Related tools' section could be more concise, and some redundancy exists with the schema (e.g., filtering information).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (7 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It explains the purpose, usage context, and provides examples, but lacks information about return format details, error conditions, authentication requirements, and doesn't fully address behavioral transparency. The absence of an output schema means the description should ideally explain what the 'Array of user activities' contains, which it only partially does through examples.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly with descriptions, examples, and constraints. The description adds minimal value beyond the schema, mentioning filtering capabilities ('Filtering: By action type, target type, date range') which the schema already covers in detail. The baseline score of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with 'Get user's activity feed' and 'Returns: Array of user activities', providing a specific verb ('Get') and resource ('user's activity feed'). However, it doesn't explicitly differentiate from sibling tools like 'gitlab_get_user_activity_feed' which appears to serve a similar purpose, though the description implies this tool offers filtering capabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a 'Use when:' section ('Tracking user contributions, audit trail') that provides clear context for when to use this tool. It also lists 'Related tools' with brief explanations ('Find users', 'Search by user'), offering some guidance on alternatives. However, it doesn't explicitly state when NOT to use this tool or compare it directly to similar siblings like 'gitlab_get_user_activity_feed'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool returns 'Updated MR object' and gives examples of updates, but lacks details on permissions needed, error conditions, rate limits, or whether updates are partial/complete. For a mutation tool with 15 parameters and no annotations, this is a moderate gap in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, returns, usage, examples, related tools) and uses bullet points efficiently. Every sentence adds value, though the examples section could be slightly more concise. Overall, it's appropriately sized and front-loaded with essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (15 parameters, mutation operation) and lack of annotations/output schema, the description is moderately complete. It covers purpose, usage, and examples but lacks details on authentication, error handling, and response format. The 100% schema coverage helps, but for a mutation tool without annotations, more behavioral context would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 15 parameters thoroughly. The description adds minimal value beyond the schema by listing example fields ('Title, description, assignees, labels, etc.') and providing usage examples. However, it doesn't explain parameter interactions or provide additional semantic context not in the schema, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Update merge request fields' with examples of specific fields (title, description, assignees, labels). It distinguishes from siblings by mentioning related tools like gitlab_close_merge_request and gitlab_merge_merge_request, though it doesn't explicitly differentiate when to use each. The verb+resource combination is specific and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with 'Use when: Modifying MR properties' and lists related tools with brief purposes (e.g., 'gitlab_close_merge_request: Just close'). It helps the agent understand when to use this tool versus alternatives, though it doesn't explicitly state when NOT to use it or provide detailed prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns ('Detailed diff of all changed files') and shows an example response format, which is helpful. However, it doesn't mention important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or error conditions. The example response adds value but doesn't fully compensate for the lack of annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and efficiently organized with clear sections: purpose statement, returns, use cases, shows, example response, and related tools. Every sentence earns its place, and the information is front-loaded with the most important details first. The example response is appropriately included to illustrate the output format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there's no output schema, the description provides a good example response that shows the structure of the returned data. The tool has 2 parameters with 100% schema coverage, and the description adds context about when to use it and what it returns. However, for a tool with no annotations, it could provide more behavioral context about safety, permissions, or limitations to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, meaning both parameters are well-documented in the schema itself. The description doesn't add any additional parameter information beyond what's already in the schema. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get commit diff/changes' followed by 'Returns: Detailed diff of all changed files'. This specifies both the action (get) and resource (commit diff/changes), and the 'Returns' statement clarifies the output. However, it doesn't explicitly differentiate from sibling tools like 'gitlab_smart_diff' beyond listing it as related.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a 'Use when:' section that provides clear context: 'Code review, understanding changes'. This gives practical guidance on when this tool is appropriate. However, it doesn't explicitly state when NOT to use it or provide direct comparisons with alternatives like 'gitlab_smart_diff' beyond listing it as related.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the scope ('across all accessible projects'), time coverage ('all time periods'), and return format details (comment details, issue info, etc.). However, it doesn't mention pagination behavior (implied by parameters but not explained), rate limits, or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with purpose, scope, return details, use cases, parameters, and example. However, it could be more front-loaded - the parameter list is extensive and might bury key information. Every sentence adds value, but some redundancy exists between parameter descriptions and schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 6-parameter tool with no annotations and no output schema, the description provides good context about what the tool does and returns. However, it lacks behavioral details like pagination mechanics, error conditions, or performance characteristics that would help an agent use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description lists parameters with brief explanations but doesn't add significant meaning beyond what's in the schema (e.g., it doesn't clarify parameter interactions like 'use either user_id or username' when user_id isn't in the schema). Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get', 'Retrieve') and resources ('comments authored by a user on issues', 'issue comments and notes'). It distinguishes from siblings like gitlab_get_issue (which gets issue details) or gitlab_get_user_activity_feed (which is broader).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context with 'across all accessible projects and time periods' and lists specific use cases (track engagement, monitor patterns, etc.). However, it doesn't explicitly state when NOT to use this tool or name alternatives like gitlab_get_user_mr_comments for merge request comments.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool returns resolved issues with details like 'Issue details', 'Resolution info', and 'Timeline', which adds behavioral context beyond basic retrieval. However, it lacks information on permissions, rate limits, or error handling, leaving gaps for a tool with 8 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections like 'Returns resolved issues with:', 'Use cases:', 'Parameters:', and an example, making it easy to scan. However, some details in the 'Returns' section (e.g., 'Recognition: contribution to project health') are somewhat verbose and could be trimmed for better conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 parameters, no annotations, no output schema), the description is moderately complete. It covers purpose, usage, and parameters but lacks details on output format, pagination behavior (beyond listing parameters), and error cases. This is adequate but has clear gaps for a tool with this level of complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description lists parameters with brief notes (e.g., 'use either user_id or username'), adding minimal value beyond the schema. This meets the baseline of 3, as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get issues closed/resolved by a user') and resource ('issues'), distinguishing it from sibling tools like 'gitlab_get_user_open_issues' or 'gitlab_list_issues' by focusing on resolved issues rather than open ones or general listings. The title 'Get issues closed/resolved by a user' reinforces this specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context with 'Use cases' (e.g., 'Track problem resolution contributions', 'Performance reviews'), which implicitly guides when to use this tool. However, it does not explicitly state when not to use it or name alternatives (e.g., 'gitlab_get_user_open_issues' for unresolved issues), missing full explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns (resolved thread information with details like timeline, impact, etc.), which is helpful. However, it lacks critical behavioral information such as whether this is a read-only operation (implied but not stated), pagination behavior (mentioned in parameters but not explained in description), rate limits, authentication requirements, or error conditions. The description adds value but leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for purpose, returns, use cases, parameters, and an example. Most sentences earn their place by adding value. However, it could be more front-loaded—the core purpose is clear early, but the detailed return list and use cases could be trimmed or integrated more tightly. The parameter list is somewhat redundant with the schema but serves as a quick reference.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 6 parameters with full schema coverage, the description is moderately complete. It explains what the tool does and what it returns, but lacks behavioral details like pagination handling, error responses, or performance considerations. The use cases help contextualize, but for a tool with multiple parameters and no structured output, more guidance on result interpretation or limitations would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description lists parameters with brief explanations (e.g., 'user_id: Numeric user ID'), but these add minimal value beyond what's in the schema. It does clarify that 'username' and 'user_id' are alternatives ('use either user_id or username'), which is useful context not in the schema. However, parameters like 'resolution_type' and 'context_type' are listed without enum values or detailed semantics, so the description doesn't fully compensate for schema limitations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get threads resolved by a user in reviews') and resource ('discussion threads'), distinguishing it from siblings like gitlab_get_user_discussion_threads (which likely gets all threads) or gitlab_get_user_resolved_issues (which focuses on issues rather than threads). The verb 'Get' combined with the qualifier 'resolved by a user' makes the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('code review effectiveness tracking', 'collaboration quality assessment', etc.), but does not explicitly state when NOT to use it or name specific alternatives among the sibling tools. For example, it doesn't contrast with gitlab_get_user_discussion_threads or gitlab_get_user_resolved_issues, which could help the agent choose more precisely.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by specifying what the tool returns ('Users with access levels'), what it shows ('Direct and inherited members'), and providing the access level mapping. However, it doesn't mention pagination behavior (implied by the parameters but not explicitly stated), rate limits, authentication requirements, or whether it's a read-only operation (though implied by 'List').
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It uses clear sections with headings ('Returns:', 'Use when:', 'Shows:', 'Access levels:', 'Related tools:'), making it easy to scan. Every sentence earns its place by providing specific, useful information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this is a read operation with no annotations, 100% schema coverage, but no output schema, the description does a good job of providing context. It explains what the tool returns, when to use it, what it shows, access level mappings, and related tools. The main gap is the lack of output format details, but for a list tool with good parameter documentation, this is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already fully documents all four parameters. The description doesn't add any parameter-specific information beyond what's in the schema. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List project members' with specific details about what it returns ('Users with access levels') and what it shows ('Direct and inherited members'). It distinguishes itself from sibling tools like gitlab_add_project_member and gitlab_update_member_role by being a read operation, though it doesn't explicitly differentiate from other list tools like gitlab_list_projects or gitlab_list_issues.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Use when: Finding team members, permissions' and lists related tools for adding or updating members. This gives clear context for when to use this tool versus alternatives like gitlab_add_project_member or gitlab_update_member_role. However, it doesn't specify when NOT to use it or mention other potential alternatives among the many sibling list tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool's function well and lists what data is returned, but doesn't mention potential limitations like rate limits, authentication requirements, data freshness, or error conditions. It adds value by specifying the aggregation scope but lacks operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, returns, use cases, parameters, example) and avoids unnecessary repetition. However, the 'Returns contribution summary including' section is somewhat verbose and could be more concise while maintaining clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only summary tool with 5 parameters and 100% schema coverage but no output schema, the description provides good context: it clearly explains what the tool does, what data it returns, use cases, and includes an example. The main gap is the lack of output schema, which the description partially compensates for by listing return data categories.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all parameters. The description adds minimal value beyond the schema by mentioning 'use either user_id or username' and providing an example, but doesn't explain parameter interactions, defaults, or constraints beyond what's in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Summarize user's recent contributions across issues, MRs, and commits' with a specific verb ('summarize') and resource ('user's contributions'). It distinguishes itself from siblings like gitlab_get_user_commits or gitlab_get_user_activity_feed by focusing on aggregated, multi-source summaries rather than individual activity streams.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool through 'Use cases' (performance reviews, team tracking, identifying contributors, project monitoring). However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools, such as gitlab_get_user_activity_feed for raw activity data instead of summaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns discussion thread information with details like engagement, origin, timeline, and impact, which adds behavioral context beyond basic functionality. However, it does not mention important behavioral traits such as authentication requirements, rate limits, error handling, or whether this is a read-only operation (though 'Get' implies it).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, returns, use cases, parameters, example) and is appropriately sized. Most sentences earn their place by adding value, though the 'Returns' section could be more concise. It is front-loaded with the core purpose, making it easy to understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, no output schema, no annotations), the description is fairly complete. It explains what the tool does, provides use cases, details parameters, and includes an example. However, it lacks information on output format, pagination behavior beyond parameters, and error scenarios, which would enhance completeness for a tool with no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description lists parameters with brief explanations (e.g., 'user_id: Numeric user ID', 'username: Username string (use either user_id or username)'), but these add minimal value beyond what's in the schema. The example provides some usage context, but overall, the description does not significantly enhance parameter understanding beyond the schema's comprehensive coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get all discussion threads started by a user') and resource ('discussion threads initiated by the specified user across issues, merge requests, and other collaborative contexts'). It distinguishes itself from sibling tools like gitlab_get_user_issue_comments or gitlab_get_user_mr_comments by focusing on threads started by the user rather than comments or other contributions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Leadership and initiative tracking', 'Communication effectiveness analysis', etc.), but does not explicitly state when not to use it or name specific alternatives among the sibling tools. It implies usage through use cases but lacks explicit exclusions or comparisons to similar tools like gitlab_get_user_resolved_threads.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool retrieves MRs 'across all accessible projects' and includes pagination behavior (via per_page and page parameters), which is useful context. However, it doesn't mention rate limits, authentication requirements, or error conditions that would be important for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, returns, use cases, parameters, example). It's appropriately sized but could be more concise by eliminating redundant parameter listings since the schema already covers them thoroughly. Every sentence adds value, but some information is duplicated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only tool with 5 parameters and no output schema, the description does a good job explaining what information is returned (MR details, status, urgency indicators, etc.) and provides use cases. However, without annotations or output schema, it could benefit from more behavioral context about limitations or error handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value beyond the schema by listing parameters and providing an example, but doesn't explain semantics like the 'priority' sort option or the relationship between user_id and username beyond what's in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get all open merge requests authored by a user' with specific verb ('Get'), resource ('open merge requests'), and scope ('authored by a user'). It distinguishes from siblings like gitlab_list_merge_requests (general listing) and gitlab_get_merge_request (single MR).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases (personal MR dashboard, team workload monitoring, etc.) that guide when to use this tool. However, it doesn't explicitly state when NOT to use it or name specific alternatives among siblings (e.g., gitlab_get_user_review_requests for different MR contexts).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by describing what the tool returns (MR details, review status, priority indicators, action items, project context) and mentions pagination behavior. However, it doesn't disclose important behavioral aspects like authentication requirements, rate limits, error conditions, or whether this is a read-only operation (though 'Get' implies it).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, returns, use cases, parameters, example). However, the parameter section duplicates schema information unnecessarily, and the use cases section could be more concise. The front-loaded purpose statement is excellent, but later sections contain some redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read operation with 6 parameters and no output schema, the description provides good context about what information is returned and how to use the tool. The absence of annotations means the description should ideally cover more behavioral aspects, but it does adequately explain the tool's purpose, usage, and expected results. The example helps clarify parameter usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema already documents all 6 parameters thoroughly. The description repeats parameter information but adds minimal additional semantic context beyond what's in the schema. The example shows parameter usage but doesn't provide new insights about parameter interactions or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get MRs where user is assigned as reviewer with pending action') and distinguishes it from sibling tools like 'gitlab_get_merge_request' or 'gitlab_get_user_open_mrs' by focusing specifically on pending review requests. The verb+resource combination is precise and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('Personal review queue/inbox', 'Team code review management', etc.) and distinguishes it by focusing on pending review assignments. However, it doesn't explicitly state when NOT to use it or name specific alternative tools for different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format in detail (event details, timestamps, project context, etc.) and mentions pagination via per_page and page parameters, which adds useful context. However, it does not cover critical aspects like authentication requirements, rate limits, error handling, or whether it's read-only/destructive, leaving gaps in transparency for a tool with 8 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for purpose, returns, use cases, parameters, and an example, making it easy to scan. It is appropriately sized for an 8-parameter tool, but some parts (like the detailed return list) could be slightly condensed without losing clarity, keeping it from a perfect score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 parameters, no annotations, no output schema), the description is moderately complete. It covers purpose, returns, use cases, and parameters adequately, but lacks details on authentication, error handling, rate limits, and exact output structure, which are important for a retrieval tool with filtering options. This leaves room for improvement in contextual coverage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds value by clarifying parameter usage: it explains that 'user_id or username' can be used interchangeably, provides an example with specific values, and lists all parameters with brief context. This enhances understanding beyond the schema, though it doesn't add deep semantic details like enum values or complex constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve user's complete activity/events timeline') and resource ('user activities including commits, issues, MRs, comments, and other interactions across all accessible projects'). It distinguishes from siblings like gitlab_list_user_events by emphasizing 'complete activity/events timeline' with detailed return structure, making the purpose specific and well-differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear use cases ('Track user engagement patterns', 'Monitor team member activities', etc.) that implicitly guide when to use this tool. However, it lacks explicit alternatives or exclusions, such as when to prefer gitlab_list_user_events or other sibling tools for simpler event listings, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the tool's behavioral traits by describing what it returns (code change summary with specific metrics) and implies it's a read-only analysis tool. However, it doesn't mention rate limits, authentication requirements, pagination behavior (though 'per_page' is in schema), or whether it's computationally expensive for large date ranges.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately front-loaded with the core purpose, but contains some redundancy (e.g., 'Get lines added/removed' then 'Generate comprehensive statistics'). The 'Use cases' section is helpful but could be more concise. The example is valuable but lengthy. Overall, it's somewhat verbose but well-structured with clear sections.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters with 100% schema coverage but no annotations and no output schema, the description does a good job explaining what the tool returns and its use cases. It provides substantial context about the analysis capabilities and metrics. However, it doesn't fully compensate for the lack of output schema by detailing the exact structure of returned data, and there's parameter inconsistency between description and schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds significant value by explaining parameter semantics beyond the schema: it clarifies that 'user_id' and 'username' are alternatives ('use either'), explains what 'include_languages' and 'include_trends' do, defines 'granularity' options, and provides a comprehensive example. However, it mentions parameters not in the schema (user_id, include_languages, include_trends, granularity), creating some inconsistency.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get lines added/removed and files changed by user over period' and 'Generate comprehensive statistics about a user's code contributions'. It distinguishes from siblings like 'gitlab_get_user_commits' (which lists commits) and 'gitlab_get_user_contributions_summary' (which might be broader) by focusing specifically on code change metrics with quantitative analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context with 'Use cases' section listing development productivity analysis, code contribution reporting, team capacity planning, and performance review data. It also includes an example showing typical parameter usage. However, it doesn't explicitly state when NOT to use this tool or name specific alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format in detail ('Returns reported issues with: - Issue details...'), which is helpful, but lacks information on permissions, rate limits, or error handling. The description does not contradict annotations, but it could be more comprehensive for a tool with 8 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose but becomes verbose with repetitive sections like 'Use cases' and 'Parameters' that largely restate information. Sentences like 'Track personal issue reporting patterns' are somewhat redundant. While structured, it could be more streamlined by eliminating overlap and focusing on unique value-add.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 parameters, no output schema, no annotations), the description does a good job of covering purpose, usage, and return details. It explains what the tool returns in a structured way, which compensates for the lack of output schema. However, it could improve by addressing potential behavioral aspects like pagination handling or error scenarios more explicitly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description lists all parameters and provides an example, but it does not add significant meaning beyond what the schema already documents (e.g., it repeats parameter names without extra context like validation rules or interdependencies). The example helps illustrate usage but doesn't enhance semantic understanding substantially.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('List all issues created/reported by a specific user') and distinguishes it from sibling tools by explicitly mentioning 'gitlab_get_user_open_issues' as an alternative for assigned issues. It specifies the scope ('including closed ones', 'across all accessible projects') and clarifies the user's role ('original reporter/creator').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool vs. alternatives, stating 'For issues currently assigned to a user, use 'gitlab_get_user_open_issues' instead.' It also includes use cases and examples that help the agent understand appropriate contexts, such as 'Bug reporting patterns' and 'User feedback analysis,' making the usage boundaries clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by describing the return format ('Returns snippet information with...') and pagination behavior (via per_page and page parameters), but doesn't mention rate limits, authentication requirements, or whether this is a read-only operation. The description implies it's a safe read operation but doesn't explicitly state this.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, returns, use cases, parameters, example) and front-loads the core functionality. However, it's somewhat verbose with detailed bullet points in the returns section that could be more concise. The parameter section lists many parameters not in the actual schema, creating some redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, 100% schema coverage, and no output schema, the description provides good context about return values and use cases. However, there's a significant mismatch between the 8 parameters described and the 3 parameters in the actual schema, creating confusion. The description compensates for the lack of output schema by detailing return information, but the parameter discrepancy reduces overall completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all 3 parameters. The description adds significant value by listing 8 parameters with semantic explanations (scope, visibility, language, sort, etc.) that aren't in the actual schema. This provides crucial context about filtering capabilities beyond the minimal required parameters, though there's a disconnect between described parameters and actual schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('List all personal and project snippets created by a user') and resource ('code snippets'), and distinguishes it from sibling tools like 'gitlab_get_snippet' (singular) and 'gitlab_list_snippets' (general listing). The title 'List all...' establishes this as a filtered listing operation focused on user-created snippets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('Find all code snippets created by the specified user across personal and project scopes') and includes use cases like 'Personal code library management' and 'Code reuse and template management.' However, it doesn't explicitly state when NOT to use it or mention alternatives like 'gitlab_list_snippets' for general snippet browsing or 'gitlab_get_snippet' for single snippet details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return type ('Array of files and directories') and includes an example response, which adds value. However, it lacks details on permissions, rate limits, or error handling, which are important for a tool interacting with a repository.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, starting with the core purpose, followed by return type, usage guidelines, optional parameters, an example response, and related tools. Each sentence adds value without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a repository browsing tool with 4 parameters and no output schema, the description is fairly complete. It covers purpose, usage, optional parameters, and provides an example response. However, it could improve by including more behavioral details like authentication needs or error cases, especially since annotations are absent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description adds minimal semantic context by listing 'Optional: Recursive listing, specific path' and mentioning related tools, but it does not provide additional meaning beyond what the schema covers, aligning with the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('browse', 'list') and resource ('repository directory structure'), and distinguishes it from siblings by mentioning related tools like gitlab_get_file_content for reading file contents and gitlab_search_in_project for searching in files, which helps differentiate its scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with 'Use when: Exploring repo structure, listing files', which clearly indicates the context for using this tool. However, it does not specify when not to use it or mention alternatives beyond the related tools, which could be more detailed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses return format with an example response, mentions sorting behavior ('Sorting: By name, date, semver'), and hints at pagination/limiting through the example array. However, it doesn't mention rate limits, authentication requirements, or whether this is a read-only operation (though 'List' implies it).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, returns, use when, sorting, example, related tools). Every sentence earns its place by providing distinct value. The example response is appropriately detailed without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read operation with no output schema, the description provides good context: clear purpose, usage guidelines, behavioral details about sorting, and a comprehensive example response. It could be more complete by explicitly stating this is a read-only operation and mentioning authentication/rate limits, but given the complexity and lack of annotations, it's mostly adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 3 parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. The baseline of 3 is appropriate when the schema does all the parameter documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('repository tags'), and distinguishes from siblings by specifying it returns 'Tags with commit info' versus gitlab_list_releases which provides 'Full release info'. This is specific and differentiates from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'Use when' section explicitly states 'Finding releases, version tags', providing clear context for when to use this tool. However, it doesn't explicitly state when NOT to use it or mention all relevant alternatives (like gitlab_list_releases is mentioned, but gitlab_list_commits or gitlab_list_branches are not compared).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the action ('Resolve a discussion thread') and return value ('Updated discussion'), which implies a mutation operation. However, it lacks details on permissions, side effects (e.g., whether it closes the thread or just marks it), or error handling, leaving behavioral gaps for an unannotated tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by returns, usage, requirements, and related tools in a bullet-like format. Each sentence adds value without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a mutation tool with 3 parameters), no annotations, and no output schema, the description does well by covering purpose, usage, prerequisites, and related tools. However, it lacks details on behavioral aspects like permissions or side effects, and the output is only briefly mentioned ('Updated discussion') without elaboration, leaving some gaps in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema fully documents the three parameters. The description adds minimal param semantics by mentioning 'Discussion ID from get_discussions' for 'discussion_id', but doesn't provide additional context beyond what's in the schema. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Resolve') and resource ('a discussion thread'), making the purpose evident. It distinguishes from siblings like 'gitlab_add_merge_request_comment' by focusing on resolution rather than commenting. However, it doesn't explicitly differentiate from tools like 'gitlab_get_user_resolved_threads' which might retrieve resolved threads, leaving some ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with 'Use when: Code review feedback addressed', indicating the appropriate context. It also specifies prerequisites with 'Required: Discussion ID from get_discussions' and lists related tools ('gitlab_get_merge_request_discussions', 'gitlab_add_merge_request_comment'), offering clear alternatives and dependencies.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool 'Modifies' fields and 'Returns: Updated snippet information', which covers basic behavioral traits. However, it lacks details on permissions required, error handling, or rate limits. The description doesn't contradict any annotations (none exist), but it provides only moderate behavioral context beyond the obvious update action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, modifications, usage context, flexibility, example, returns, related tools) and uses bullet points for readability. It is appropriately sized, but some redundancy exists (e.g., listing fields in 'Modifies' and the example). Overall, it's efficient with minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (update operation with 7 parameters), no annotations, and no output schema, the description is moderately complete. It covers purpose, usage, parameters via example, and return info, but lacks details on behavioral aspects like authentication needs or error responses. It's adequate for basic use but has gaps for a mutation tool without structured support.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds minimal value beyond the schema by listing modifiable fields ('Title, content, file name, description, or visibility') and providing an example usage. This meets the baseline of 3, as the schema does the heavy lifting, but the description doesn't add significant semantic depth.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Update') and resource ('existing snippet'), and distinguishes it from sibling tools like gitlab_create_snippet (for creating new snippets) and gitlab_get_snippet (for viewing before updating). The opening line 'Update existing snippet' is direct and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('Use when: Fixing code, updating examples, changing permissions') and includes a 'Related tools' section that names alternatives (gitlab_get_snippet for viewing before updating, gitlab_create_snippet for creating new instead). This clearly differentiates it from sibling tools and provides context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It implies a read-only operation by using 'Get' and lists return fields, but does not disclose behavioral traits like authentication requirements, rate limits, error handling, or whether it's idempotent. The example response adds some context but lacks operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement, bulleted details, use cases, and an example, all in a compact format. Every sentence adds value, but it could be slightly more concise by integrating the example into the bullet points or reducing redundancy in the use cases.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is quite complete. It explains what the tool does, what it returns, and when to use it. However, it lacks some behavioral context (e.g., authentication needs, error cases), which prevents a perfect score despite the low complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics by listing return fields and providing an example, adding value beyond the schema. A baseline of 4 is given since no parameters exist, and the description compensates well for the lack of output schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('currently authenticated user's profile'), distinguishing it from sibling tools like 'gitlab_get_user' or 'gitlab_get_my_profile' by specifying it returns the authenticated user's own data. The detailed bullet points reinforce what information is retrieved.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases (e.g., 'Verify authentication is working', 'Get user context for automation scripts'), which offer clear guidance on when to use this tool. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as 'gitlab_get_user' for other users' data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool retrieves issues 'across all accessible projects' (scope), uses 'intelligent priority sorting' (sorting logic), returns a 'prioritized issue list' with detailed fields (output structure), and supports pagination via per_page and page parameters. It also mentions filtering capabilities and default values. While it doesn't cover rate limits or authentication needs, it provides substantial behavioral context beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, usage, returns, use cases, parameters, example) and front-loads the core functionality. However, it includes some redundancy (e.g., listing parameters that are fully documented in the schema) and the 'Use cases' section, while helpful, adds length without critical new information. Most sentences earn their place, but minor trimming could improve efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (7 parameters, no output schema, no annotations), the description provides strong contextual completeness. It covers purpose, usage, behavioral traits, output structure, use cases, and parameters with an example. The main gap is the lack of an output schema, but the description compensates by detailing the return format ('Returns prioritized issue list with...'). It could be more explicit about error handling or permissions, but overall it's highly informative.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema: it clarifies that either user_id or username can be used ('use either user_id or username'), provides an example usage, and lists parameters with brief notes. However, it doesn't explain parameter interactions or add significant semantic context that isn't already in the schema descriptions, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List open issues assigned to or created by a specific user') and resource ('issues'), distinguishing it from sibling tools like gitlab_get_issue (single issue) or gitlab_list_issues (general listing). It explicitly mentions the scope ('across all accessible projects') and purpose ('see what issues a user is currently working on or responsible for'), providing excellent differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Use this tool to see what issues a user is currently working on or responsible for') and lists specific use cases (personal dashboard, workload management, SLA tracking, sprint planning). However, it doesn't explicitly state when NOT to use it or name alternative tools for similar purposes, such as gitlab_get_user_reported_issues or gitlab_get_user_resolved_issues, which could help avoid confusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the return type ('Raw file content as string') and default behavior ('defaults to default branch'), but doesn't mention error conditions, rate limits, authentication requirements, or what happens with binary files. It provides basic behavioral context but lacks comprehensive disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with core purpose and return value. Each section ('Returns:', 'Use when:', 'Optional:', 'Example:', 'Related tools:') adds specific value without redundancy. Every sentence earns its place in helping the agent understand the tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only tool with 100% schema coverage but no output schema, the description provides good context about usage scenarios, examples, and related tools. However, without annotations or output schema, it could better address error cases or limitations. The completeness is strong but not perfect given the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value beyond the schema - it mentions the optional ref parameter defaults to default branch, which is already in the schema. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get file content from repository') and resource ('file'), distinguishing it from siblings like gitlab_list_repository_tree (browsing) and gitlab_create_commit (modifying). The examples further clarify the purpose by showing different file types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is provided with 'Use when: Reading source code, configs, documentation' and 'Related tools' section that names alternatives for browsing files, modifying files, and seeing changes. This gives clear context for when to use this tool versus other options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by detailing what it returns (commit details, code changes, context, author info, statistics), mentions pagination behavior via 'per_page' and 'page' parameters, and specifies filtering capabilities. However, it doesn't explicitly mention rate limits, authentication requirements, or potential side effects, leaving some behavioral aspects uncovered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and usage guidance, but it becomes repetitive with multiple sections (e.g., 'Use cases' and 'Examples' overlap, and the parameter list duplicates schema info). Some sentences, like the second bullet under 'Use cases', could be condensed or removed to improve efficiency without losing value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (9 parameters, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, behavior, and parameters, but lacks details on output format (only lists return categories without structure) and doesn't address potential errors or limitations. It compensates well for the absence of annotations and output schema, but could be more thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by listing parameters with brief explanations and providing an example, but it doesn't add significant semantic context or clarify interdependencies (e.g., 'user_id' vs. 'username'). Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('List all commits authored by a specific user') and resources ('commits', 'user'), and distinguishes it from sibling tools by explicitly mentioning 'gitlab_get_user_merge_commits' as an alternative for merge commits. The bolded sentence reinforces the core purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool vs. alternatives, stating 'For merge commits specifically, use 'gitlab_get_user_merge_commits' instead.' It also lists multiple use cases (e.g., code contribution analysis, developer productivity metrics) and includes an example, giving clear context for application.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool returns ('detailed information about a user's GitLab activity'), including specific categories like profile, statistics, activity, settings, and social links. However, it lacks details on error handling, rate limits, or authentication requirements, which would elevate the score further.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose but becomes verbose with redundant sections like 'Use cases' and 'Returns extended user information' that repeat earlier points. While informative, it could be more streamlined by eliminating repetition, such as merging the activity summary with the detailed return list.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a user details tool with no annotations and no output schema, the description does a good job of covering purpose, usage, and return values. It includes examples, parameter guidance, and sibling tool differentiation. However, it could improve by specifying output format or error cases to be fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters (user_id and username). The description adds minimal value beyond the schema by noting 'use either user_id or username' and providing an example, but it doesn't clarify exclusivity or priority rules. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get comprehensive activity summary and contributions') and resources ('for a specific user'). It explicitly distinguishes itself from sibling tools like 'gitlab_get_user' for basic profile info and 'gitlab_search_user' for finding users, making the differentiation clear and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('when you need detailed insights into a user's GitLab activity and contributions') and when to use alternatives ('For basic user profile info, use 'gitlab_get_user' instead. For finding users by search, use 'gitlab_search_user' instead.'). It also includes use cases and examples, offering comprehensive context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions what the tool returns ('Results from specified scope', 'Returns matching issues with highlights') and provides an example, but doesn't cover important behavioral aspects like authentication requirements, rate limits, error conditions, or pagination behavior (though pagination parameters exist in the schema). The description adds some context but leaves gaps for a search operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, returns, use when, required, scopes, example, related tools) and every sentence earns its place. It's appropriately sized for a search tool with multiple scopes and provides essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 5 parameters, 100% schema coverage, and no output schema, the description provides good contextual completeness. It covers purpose, usage guidelines, scope options, and relationships to other tools. The main gap is the lack of output format details (no output schema exists), but the description mentions what gets returned ('Results from specified scope', 'Returns matching issues with highlights').
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 5 parameters. The description adds minimal value beyond the schema - it mentions 'Required: Scope (what to search in)' and lists scopes, but the schema already provides this information with more detail. The description doesn't add meaningful parameter semantics beyond what's in the comprehensive schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search within a project') and resource ('project'), distinguishing it from sibling tools like 'gitlab_search_projects' (across projects) and specific list tools for each type. The opening line 'Search within a project' provides immediate clarity about scope and function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Use when: Finding issues, MRs, code, wiki pages' and provides a 'Related tools' section that names the alternative 'gitlab_search_projects' for cross-project searching. This gives clear guidance on when to use this tool versus its sibling alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It describes the return format ('Concise summary for LLM context') and optimization goals ('Limited context windows', 'Quick understanding'), but doesn't mention important behavioral aspects like whether this makes API calls, potential rate limits, authentication requirements, or error conditions. It provides some context but leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely well-structured with clear sections (Returns, Use when, Includes, Optimized for, Related tools). Every sentence earns its place by providing distinct value. The information is front-loaded with the core purpose first, followed by supporting details. No wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only summary tool with 3 parameters and 100% schema coverage, the description provides good contextual completeness. It explains the tool's purpose, when to use it, what it includes, optimization goals, and related alternatives. The main gap is the lack of output schema, but the description does specify 'Returns: Concise summary for LLM context' which provides some output guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema already thoroughly documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline score of 3 is appropriate since the schema does the heavy lifting, though the description could have provided additional context about parameter interactions or usage patterns.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate AI-friendly MR summary' with specific verb ('Generate') and resource ('MR summary'). It distinguishes from siblings by explicitly contrasting with 'gitlab_get_merge_request: Full details' and 'gitlab_summarize_issue: Issue summaries', making it clear this is a specialized summary tool for merge requests.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with 'Use when: Reviewing MRs with AI assistance' and 'Related tools' section that names alternatives. It clearly indicates when to use this tool (for AI-assisted MR review) versus when to use other tools (for full details or issue summaries), providing excellent context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden of behavioral disclosure. It does well by explaining key behavioral traits: atomic nature ('All changes or none'), operation limits ('Up to 100 operations'), supported action types, and return value ('Returns: New commit details'). However, it doesn't mention authentication requirements, rate limits, or error handling scenarios.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (description, returns, use when, supports, key features, example, related tools) and front-loaded with the core purpose. While slightly longer than minimal, every section adds value. The example is particularly helpful but makes the description less concise than ideal.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with 6 parameters, 100% schema coverage, but no annotations or output schema, the description does quite well. It covers purpose, usage context, behavioral traits, and provides a concrete example. The main gap is the lack of output details (only 'New commit details' is mentioned without structure), but given the complexity and schema richness, this is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description adds minimal parameter semantics beyond the schema - it mentions 'Multiple files: Up to 100 operations' which aligns with the schema's max items constraint, and provides an example showing the actions array structure. However, it doesn't add significant meaning beyond what's already documented in the comprehensive schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Create commit with file changes') and distinguishes it from siblings by mentioning atomic operations and multiple file capabilities. It explicitly differentiates from gitlab_safe_preview_commit and gitlab_list_repository_tree in the 'Related tools' section.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with 'Use when: Making changes via API' and offers clear alternatives in the 'Related tools' section (gitlab_safe_preview_commit for previewing first, gitlab_list_repository_tree for checking existing files). This gives the agent specific when-to-use and when-not-to-use information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns threaded discussions with replies (output format), and the example response shows structure including resolvable status. However, it doesn't mention pagination behavior, rate limits, or authentication requirements, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with purpose, usage guidance, example, and related tools sections. It's appropriately sized, but the example response is quite detailed and could be summarized more concisely. Most sentences earn their place, though some formatting could be tighter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description does a good job explaining what the tool returns (threaded discussions with replies) and when to use it. The example response provides concrete output structure. However, it doesn't cover error cases, authentication needs, or pagination behavior, which would be helpful for a read operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no parameter-specific information beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Get') and resource ('MR discussion threads'), and distinguishes it from sibling tools like 'gitlab_get_merge_request_notes' by emphasizing thread structure. The opening line 'Get MR discussion threads' is direct and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage guidance with 'Use when: Reading code review comments' and 'Better than notes: Shows thread structure', which differentiates it from the 'gitlab_get_merge_request_notes' sibling tool. It also lists related tools for follow-up actions, offering clear alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that the tool returns 'Complete project metadata, settings, statistics' and provides an example response, which adds valuable behavioral context. However, it doesn't mention potential limitations like authentication requirements, rate limits, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, returns, usage, required, example, related tools) and front-loaded key information. It's appropriately sized, though the example response is detailed but necessary for clarity. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no nested objects) and 100% schema coverage, the description is mostly complete. It provides purpose, usage guidelines, example output, and sibling differentiation. However, with no output schema and no annotations, it could benefit from more behavioral details like error handling or authentication needs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the 'project_id' parameter thoroughly with examples and format details. The description adds minimal value beyond stating 'Required: Project ID or path', which is already covered in the schema. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'detailed project information', making the purpose specific. It distinguishes from siblings like 'gitlab_list_projects' (find projects) and 'gitlab_get_current_project' (auto-detect), establishing clear differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage guidance with 'Use when: Need full project details, checking configuration' and lists related tools with their purposes. It clearly indicates when to use this tool versus alternatives like 'gitlab_list_projects' for finding projects.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns 'Complete snippet information with content' and includes an example response showing the structure. However, it doesn't mention potential errors (e.g., if snippet_id is invalid), rate limits, or authentication needs, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections like 'Returns', 'Use when', 'Content', and 'Related tools', making it easy to scan. It includes an example response, which is helpful but adds length. Some redundancy exists (e.g., 'Content: Full text content included' could be merged), but overall it's efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is fairly complete. It explains the purpose, usage, and output via an example. However, it lacks details on error handling or authentication, which would be beneficial for full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter information beyond what the schema provides (e.g., no clarification on project_id auto-detection or snippet_id sourcing). This meets the baseline of 3 when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get snippet details and content') and resource ('snippet'), distinguishing it from siblings like gitlab_list_snippets (browsing) and gitlab_update_snippet (modifying). It explicitly mentions what is returned ('Complete snippet information with content'), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes an explicit 'Use when' section ('Reading snippet code, reviewing implementations'), providing clear context for when to invoke this tool. It also lists related tools with brief descriptions (e.g., gitlab_list_snippets for browsing, gitlab_update_snippet for modifying), offering alternatives and differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses key behavioral traits: the tool returns pipeline runs with status, lists available statuses with explanations, provides a detailed example response format, and mentions filtering capabilities. It doesn't cover rate limits, authentication needs, or pagination behavior, but provides substantial operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections (description, returns, use when, filtering, statuses, example, related tools). Each section earns its place, though the status list and example response are somewhat verbose for a description. The information is front-loaded with the core purpose first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only listing tool with no output schema, the description provides excellent context: clear purpose, usage guidelines, behavioral details (status explanations, example response), and sibling relationships. It doesn't cover all possible edge cases or pagination, but gives the agent enough to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description mentions filtering 'By ref (branch), status' which aligns with the 'ref' parameter but doesn't add meaningful semantic information beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('CI/CD pipelines') with specific scope. It distinguishes from sibling tools by mentioning 'gitlab_get_pipeline' for full details and 'gitlab_summarize_pipeline' for AI-friendly summaries, establishing its role as a listing tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit 'Use when' section provides clear context ('Checking CI status, finding failures'). It names specific alternative tools ('gitlab_get_pipeline', 'gitlab_summarize_pipeline') for different use cases, giving the agent explicit guidance on when to choose this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it specifies the return format ('Array of jobs'), mentions pagination behavior ('Yes (default 20 per page)'), describes filtering capabilities ('By job status/scope'), and provides a detailed example response showing structure. It doesn't mention authentication requirements or rate limits, but covers most operational aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, returns, use when, pagination, filtering, example, related tools) and every sentence earns its place. It could be slightly more concise by integrating some information, but overall it's efficiently organized and front-loaded with key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only listing tool with 4 parameters and no output schema, the description provides excellent context: clear purpose, usage guidelines, behavioral details (pagination, filtering), example response, and sibling tool differentiation. The main gap is no authentication or rate limit information, but otherwise it's quite complete for this tool type.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 4 parameters. The description adds minimal parameter semantics beyond what's in the schema - it mentions filtering options generally but doesn't provide additional syntax or format details. This meets the baseline of 3 when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and resource 'all jobs for a project', specifying it returns an array of jobs across all pipelines with filtering options. It explicitly distinguishes from sibling tools by mentioning 'gitlab_list_pipeline_jobs: Jobs for specific pipeline' and 'gitlab_list_pipelines: Find pipeline information', showing clear differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit 'Use when' guidance: 'Monitoring project CI/CD, finding recent failures, browsing job history'. It also names specific alternative tools for different use cases in the 'Related tools' section, giving clear when-to-use and when-not-to-use context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behaviors: it returns an array with metadata, supports pagination (default 20 per page), and filters by project. It also provides a detailed example response format. However, it doesn't mention rate limits, authentication requirements, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Returns, Use when, Pagination, Filtering, Example response, Related tools) and uses bullet points efficiently. Every sentence earns its place, though the example response is quite detailed, which is helpful but adds length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list tool with no annotations and no output schema, the description provides good context: purpose, usage, pagination behavior, filtering scope, example response format, and related tools. It covers the essential aspects an agent needs to invoke it correctly, though it could mention authentication or error scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, but it does mention 'Filtering: By project' which aligns with the project_id parameter. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and resource 'project snippets', distinguishing it from sibling tools like gitlab_get_snippet (for content) and gitlab_create_snippet (for creation). The title 'List project snippets' is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Use when: Browsing code snippets, finding reusable code' and lists related tools with their specific purposes (gitlab_get_snippet for content, gitlab_create_snippet for creation). This provides clear guidance on when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by describing the return format (user information including basic details, public profile, activity status), pagination behavior, and search capabilities. It doesn't mention rate limits, authentication requirements, or error conditions, but provides substantial behavioral context for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, usage guidance, examples, returns, sibling tool reference, parameters, and example). Some redundancy exists between the description text and the parameter section, but overall it's efficiently organized with each section serving a distinct purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 3 parameters and no output schema, the description provides comprehensive context: clear purpose, usage guidelines, return format description, parameter explanations, and sibling tool differentiation. The main gap is the lack of output schema, but the description compensates by detailing what information is returned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all three parameters. The description repeats parameter information in a separate section but doesn't add meaningful semantic context beyond what's in the schema. The examples help illustrate usage but don't enhance parameter understanding beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Search for') and resource ('GitLab users'), and explicitly distinguishes it from its sibling 'gitlab_get_user' by explaining it's for partial information searches rather than exact lookups. The title is null, so the description fully defines the tool's function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('when you don't have the exact username or ID'), when not to use it (use 'gitlab_get_user' for exact ID/username), and gives concrete examples of search scenarios. The bolded statement reinforces the primary use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well: it describes the return format ('Condensed issue information'), smart truncation behavior ('Preserves key information, Removes redundancy, Fits context limits'), and includes contextual notes about what gets included (title, description, comments, status). It doesn't mention rate limits or authentication requirements, but provides substantial behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Returns, Use when, Includes, Smart truncation, Related tools) and uses bullet points efficiently. Every sentence earns its place, though the 'Smart truncation' section could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, 100% schema coverage, but no annotations and no output schema, the description provides good context: purpose, usage guidelines, behavioral traits, and sibling differentiation. It doesn't describe the exact output format or error conditions, but covers most essential aspects given the structured data available.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), baseline is 3 even with no param info in description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate AI-friendly issue summary' with specific components included (title, description, comments, status). It distinguishes from sibling gitlab_get_issue by emphasizing condensed vs full details, and from gitlab_summarize_pipeline by specifying issue vs pipeline focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage guidance: 'Use when: Processing issues with AI' and 'Related tools:' section names specific alternatives (gitlab_get_issue for full details, gitlab_summarize_pipeline for pipeline summaries). This gives clear when-to-use and when-not-to-use context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by stating the return type ('Created comment object'), supported features ('Supports: Markdown, mentions, references'), and providing an example. However, it lacks details on permissions, error conditions, or rate limits, which would be helpful for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by return value, usage guidelines, features, an example, and related tools. Each section is concise and adds value without redundancy, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, mutation operation) and lack of annotations or output schema, the description does a good job covering purpose, usage, and behavior. It includes an example and related tools, but could improve by mentioning authentication needs or potential side effects, which are relevant for a comment-adding tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description does not add any parameter-specific information beyond what the schema provides, such as explaining the relationship between parameters or additional constraints. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Add comment to issue') and resource ('issue'), distinguishing it from sibling tools like gitlab_add_merge_request_comment. It specifies the return value ('Created comment object'), making the purpose explicit and distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage context with 'Use when: Providing feedback, updates' and lists related tools ('gitlab_get_issue: Read issue first', 'gitlab_list_issues: Find issues'), offering clear guidance on when to use this tool and what alternatives to consider for prerequisite steps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully adds valuable context beyond basic functionality: it specifies the return value ('Returns: Created comment object'), mentions supported features ('Supports: Markdown, mentions, slash commands'), and provides a concrete example. However, it doesn't mention permissions, rate limits, or error conditions that would be helpful for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with clear sections: purpose statement, return value, usage guidance, supported features, example, and related tools. Every sentence earns its place by providing distinct, valuable information without redundancy. The information is front-loaded with the core functionality first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with 3 parameters, 100% schema coverage, but no annotations or output schema, the description does a good job of providing context. It covers purpose, usage, return value, and supported features. However, as a write operation, it could benefit from mentioning authentication requirements or potential side effects that aren't covered by the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is high, but doesn't provide additional semantic context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a clear verb+resource statement 'Add comment to merge request' that precisely states what the tool does. It distinguishes this tool from sibling tools like gitlab_get_merge_request_notes (read existing) and gitlab_get_merge_request_discussions (threaded view), making the purpose specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage guidance with 'Use when: Code review feedback, discussions' and lists related tools with their specific purposes ('Read existing', 'Threaded view'). This gives clear context about when to use this tool versus alternatives, including both positive guidance (when to use) and implicit exclusions (when not to use).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the action ('Approve'), a constraint ('Cannot approve your own MRs'), and the return value ('Approval status'). However, it lacks details on permissions required, error conditions, or rate limits, which would be helpful for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with clear sections: purpose, returns, usage context, constraint, and related tools. Every sentence adds value without redundancy. It's front-loaded with the core action and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, mutation operation) and no annotations or output schema, the description does well by covering purpose, usage guidelines, constraints, and alternatives. However, it could provide more behavioral context (e.g., what happens on success/failure, authentication requirements) to be fully complete for a mutation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing detailed documentation for both parameters. The description adds no parameter-specific information beyond what the schema already covers. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Approve a merge request') and resource ('merge request'), distinguishing it from sibling tools like 'gitlab_get_merge_request_approvals' (check status) and 'gitlab_merge_merge_request' (merge after approval). The verb 'approve' is precise and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use ('Code review complete, changes approved') and when not to use ('Cannot approve your own MRs'), and provides clear alternatives ('gitlab_get_merge_request_approvals: Check status', 'gitlab_merge_merge_request: Merge after approval'). This gives comprehensive guidance for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: 'Returns: New commit on target branch' and 'Creates: New commit with same changes', clarifying it's a write operation that produces a new commit. However, it doesn't mention potential side effects like merge conflicts, permissions required, or error conditions, leaving some behavioral aspects uncovered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with key information: purpose, returns, usage, and creation. Each section (e.g., 'Use when:', 'Example:', 'Related tools:') is concise and adds value without redundancy. The example is brief yet illustrative, making efficient use of space.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a write operation with 3 parameters) and no annotations or output schema, the description does a good job covering purpose, usage, and behavior. However, it lacks details on error handling, authentication requirements, or what happens in edge cases (e.g., if the target branch doesn't exist), which would enhance completeness for a mutation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing detailed documentation for all three parameters (project_id, commit_sha, branch). The description adds minimal parameter semantics beyond the schema, only implying through the example that 'commit' maps to commit_sha and 'branch' maps to the target branch. It doesn't provide additional context or usage nuances for the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Apply commit to another branch') and resource ('commit'), distinguishing it from siblings like gitlab_create_commit (which creates new commits) and gitlab_get_commit (which retrieves commit info). The first sentence provides a precise verb+resource combination.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Use when: Backporting fixes, selective changes' and provides a concrete example ('Example: Backport bug fix to stable'). It also lists related tools (gitlab_get_commit, gitlab_create_merge_request) to guide users toward alternatives or complementary actions, offering clear context for when to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the action ('Close merge request without merging'), the result ('Returns: Updated MR with closed state'), and important behavioral traits ('Note: Can be reopened later'). However, it doesn't mention potential side effects like notifications, permission requirements, or error conditions, leaving some gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with clear sections: action, return value, usage context, important note, and related tools. Every sentence adds value without redundancy, and information is front-loaded with the core purpose stated first. The bulleted related tools section is particularly helpful for navigation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description does well by explaining the action, return value, usage context, and reversibility. However, it doesn't cover potential error cases, authentication requirements, or rate limits. Given the complexity of closing a merge request, some additional context about permissions or side effects would make it more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with detailed explanations for both parameters (project_id and mr_iid). The description adds no parameter-specific information beyond what's already in the schema. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Close merge request without merging') and distinguishes it from sibling tools like 'gitlab_merge_merge_request' (for merging) and 'gitlab_update_merge_request' (for reopening or other updates). It explicitly identifies the resource (merge request) and the verb (close without merging), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('Use when: Abandoning changes, deferring work') and clearly names alternatives ('gitlab_update_merge_request: Reopen or other updates' and 'gitlab_merge_merge_request: Merge instead'). This gives the agent clear context for selecting this tool over others in the sibling set.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by stating what the tool returns ('commits and diffs between refs') and showing what it does ('Shows: What changed between two points'). However, it doesn't mention potential limitations like rate limits, authentication requirements, or whether this is a read-only operation (though 'compare' implies non-destructive).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly structured and concise. It uses bullet points and clear sections ('Returns:', 'Use when:', 'Shows:', 'Example:', 'Related tools:') with zero wasted words. Every sentence earns its place by providing essential information in a scannable format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a comparison tool with no output schema and no annotations, the description does well by explaining what the tool returns and when to use it. However, it could be more complete by mentioning the output format (e.g., JSON structure of commits/diffs) or any limitations. The example helps, but without an output schema, more detail about the return value would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 4 parameters. The description adds minimal parameter semantics beyond the schema - it provides an example with 'from: "main"' and 'to: "feature/new-feature"' but doesn't explain parameter relationships or constraints beyond what's in the schema descriptions. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Compare two git references' with specific verbs and resources. It distinguishes from siblings by explicitly mentioning what it returns ('commits and diffs between refs') and provides a concrete example comparing 'feature branch to main', making the purpose unambiguous and distinct from related tools like gitlab_smart_diff.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Use when: Reviewing changes before merge', providing clear context for when to use this tool. It also lists related tools with specific guidance: 'gitlab_create_merge_request: Create MR from comparison' and 'gitlab_smart_diff: Advanced diff options', giving clear alternatives for different use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It clearly indicates this is a creation/mutation tool ('Creates: New snippet'), specifies required vs. optional parameters, describes the return value ('Returns: Created snippet with ID and URLs'), and provides an example. It doesn't mention authentication needs, rate limits, or error conditions, but covers the essential behavioral aspects well for a creation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, creates, use when, required/optional parameters, example, returns, related tools). Every sentence adds value - there's no redundant information, and the example is appropriately brief yet illustrative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a creation tool with no annotations and no output schema, the description does an excellent job covering purpose, usage, parameters, and expected return. It provides an example and related tool guidance. The main gap is the lack of output schema, but the description compensates by describing the return value. It doesn't cover error cases or authentication requirements, which keeps it from a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 6 parameters. The description lists required and optional parameters but doesn't add meaningful semantic context beyond what's in the schema (e.g., it doesn't explain relationships between parameters or provide additional usage nuances). This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Create a new code snippet') and resource ('snippet with specified content and metadata'), distinguishing it from sibling tools like gitlab_update_snippet (modify) and gitlab_list_snippets (view). The opening line is direct and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides 'Use when' scenarios (saving reusable code, sharing solutions, documenting examples) and lists 'Related tools' with specific guidance on when to use alternatives (modify after creation, view created snippets). This gives clear context for when to choose this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: that it returns metadata only (not actual file downloads), mentions security implications, lists what content is returned (artifacts with sizes and expiration), and provides a detailed example response. The only minor gap is lack of explicit mention about whether this is a read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and efficiently organized with clear sections (Returns, Use when, Security, Content, Example response, Related tools). Every sentence earns its place, providing essential information without redundancy. The information is front-loaded with the core purpose immediately stated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description provides substantial context: clear purpose, usage guidelines, behavioral details, security notes, example response, and related tools. The main gap is the lack of explicit output schema documentation, though the example response partially compensates. Given the complexity and lack of structured fields, this is quite comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema already comprehensively documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions, so it meets the baseline expectation without providing additional value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get information about job artifacts') and distinguishes it from actual file downloads. It explicitly mentions what it returns ('Artifact metadata and download information') and differentiates from sibling tools like gitlab_list_pipeline_jobs by focusing on artifact details rather than job listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with a 'Use when:' section listing specific scenarios (checking build outputs, downloading test results, accessing reports). It also references related tools for finding job IDs and browsing jobs, clearly establishing when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool returns 'Complete commit information with stats' and includes an example response showing the structure and content. However, it doesn't mention error conditions, rate limits, or authentication requirements, which are typical behavioral traits for API tools.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with key information (purpose, returns, usage, required parameter). Each section ('Returns:', 'Use when:', 'Required:', 'Example response:', 'Related tools:') adds value without redundancy. The example response is illustrative but not overly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description provides good context: purpose, usage guidelines, example response, and related tools. It covers most needs for a read operation, though it lacks details on error handling or authentication, which would be helpful for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema, only noting 'Required: Commit SHA (short or full)' which is already in the schema. No additional semantic context is provided for parameters like 'project_id' or 'include_stats'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('single commit details'), distinguishing it from siblings like 'gitlab_list_commits' (browse history) and 'gitlab_get_commit_diff' (view changes). The opening line 'Get single commit details' is precise and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Use when: Examining specific commit' and provides a 'Related tools' section that names alternatives (e.g., 'gitlab_list_commits: Browse history'), giving clear guidance on when to use this tool versus others. This helps the agent select the right tool for the task.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It explains the multi-step process: checks git remotes, identifies GitLab URLs, fetches project details from API. It also states 'No parameters needed' (though there is an optional path parameter) and mentions what it returns. However, it doesn't disclose error handling, authentication requirements, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Returns, Use when, No parameters needed, How it works, Related tools), front-loads key information, and every sentence earns its place. It's appropriately sized for the tool's complexity without unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (auto-detection logic), no annotations, and no output schema, the description does well by explaining the detection process, usage context, and return value reference. However, it could better address the optional path parameter discrepancy and provide more detail on error cases or authentication requirements.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents the single optional 'path' parameter. The description adds minimal value beyond the schema: it mentions 'Examines git remotes' and 'No parameters needed' (contradicted by the schema's optional path), but doesn't provide additional semantic context about parameter usage or implications.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Auto-detect project from git repository' - a specific verb ('auto-detect') and resource ('project from git repository'). It distinguishes from siblings by explaining it examines git remotes automatically, unlike gitlab_get_project which requires a known project ID.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Use when: Working in a git repo with GitLab remote' and lists two related tools with clear alternatives: gitlab_get_project for when you know the project ID, and gitlab_list_projects for browsing available projects. This gives clear when-to-use and when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'complete group metadata, settings, statistics' and includes an example response, which adds context about output format. However, it doesn't mention behavioral traits like rate limits, authentication needs, or error handling, leaving some gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections like 'Returns', 'Use when', 'Optional', and 'Related tools', making it easy to scan. Each sentence adds value without waste, such as clarifying usage and providing an example, resulting in an efficient and front-loaded presentation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description compensates well by including an example response and usage guidelines. It covers the tool's purpose, when to use it, and related tools, but lacks details on error cases or performance limits, which could be useful for a read operation with no structured output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds minimal value by mentioning 'with_projects=true' includes 'first page of projects', but this is largely redundant with the schema's detailed description. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'detailed group information', specifying it returns 'complete group metadata, settings, statistics'. It distinguishes from siblings like 'gitlab_list_groups' (browse groups) and 'gitlab_list_group_projects' (list projects), making the purpose specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'Use when' section explicitly states scenarios: 'Need full group details, checking configuration, counting projects'. It also names related tools as alternatives, providing clear guidance on when to use this tool versus others, such as for detailed info rather than browsing or listing projects.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns full MR data including pipelines, approvals, and conflicts, and specifies required parameters (MR IID). However, it doesn't mention rate limits, authentication needs, or error handling, leaving some gaps for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with key information (purpose, returns, usage), followed by helpful details (parameter explanation, example, related tools). Every sentence earns its place without redundancy, making it efficient and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (read operation with 2 parameters) and no output schema, the description provides a comprehensive example return structure, which compensates well. It covers purpose, usage, parameters, and sibling relationships, though it lacks details on authentication or error cases, which are minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds minimal value beyond the schema by explaining 'What's IID?' and providing an example, but doesn't add significant semantic context. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get complete merge request details') and resource ('merge request'), and distinguishes it from siblings by listing related tools like gitlab_get_merge_request_changes for diffs and gitlab_get_merge_request_discussions for reviews. This provides precise differentiation beyond just the name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use it ('Use when: Reviewing MR, checking merge status') and lists related tools as alternatives for specific needs (e.g., 'gitlab_get_merge_request_changes: See actual diffs'). This gives clear guidance on context and when to choose other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly indicates this is a read operation ('Check', 'Returns', 'Shows') and provides a detailed example response showing the data structure. However, it doesn't mention potential errors, rate limits, or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly structured with clear sections: purpose, returns, usage, example response, and related tools. Every sentence earns its place, and the information is front-loaded with the core purpose first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only tool with no output schema, the description provides excellent context including a detailed example response. It covers purpose, usage, and relationships to other tools. The main gap is the lack of error handling or edge case information, but otherwise it's quite complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, providing comprehensive documentation for both parameters. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Check') and resource ('MR approval status'), and distinguishes it from siblings by showing what it returns. It explicitly differentiates from gitlab_approve_merge_request and gitlab_merge_merge_request, making its read-only nature clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with 'Use when: Checking if MR can be merged' and lists related tools with their purposes. This gives clear context for when to use this tool versus alternatives like approving or merging.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns 'Complete diffs for all files', 'Full file diffs with context', and includes 'all commits in the MR'. However, it doesn't mention potential limitations like rate limits, authentication needs, or pagination behavior, which would be helpful for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with bullet-like sections (Returns, Use when, Shows) and a related tools list. Every sentence adds value: the first line states the purpose, subsequent lines provide context and comparisons, and the final section clarifies sibling relationships. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (read operation with 2 parameters), no annotations, and no output schema, the description does a good job explaining what the tool returns ('Complete diffs', 'Full file diffs with context') and when to use it. However, it could benefit from more detail on output format or error conditions to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing detailed documentation for both parameters. The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline of 3. It doesn't compensate but doesn't need to given the comprehensive schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get detailed MR file changes') and resources ('MR file changes', 'Complete diffs for all files'). It effectively distinguishes from siblings like 'gitlab_get_merge_request' (overview) and 'gitlab_smart_diff' (customizable diffs) by specifying it returns complete diffs for all files in the MR.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with 'Use when: Reviewing code changes' and lists related tools with clear distinctions ('gitlab_get_merge_request: MR overview', 'gitlab_smart_diff: Customizable diffs'). This tells the agent precisely when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a read operation (implied by 'List'), includes pagination details ('Yes (default 10 per page)'), sorting options ('By created_at or updated_at'), and provides an example response structure. However, it doesn't mention rate limits, authentication needs, or error handling, leaving some gaps for a tool with 7 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded: it starts with the core purpose, followed by key behavioral details (returns, use when, pagination, sorting), an example response, and related tools. Each section is brief and informative, with no wasted sentences. The formatting (bulleted lists in the response) enhances readability without verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, no annotations, no output schema), the description is largely complete: it covers purpose, usage, pagination, sorting, and provides an example response. However, it lacks details on error cases, rate limits, or authentication requirements, which are important for a read operation in a GitLab context. The example response helps but doesn't fully substitute for an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds no specific parameter semantics beyond what's in the schema (e.g., it doesn't explain how 'max_body_length' interacts with the example response). This meets the baseline of 3 since the schema does the heavy lifting, but the description doesn't compensate with additional insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a clear verb ('List') and resource ('merge request comments'), specifying the exact scope. It distinguishes from sibling tools like 'gitlab_get_merge_request_discussions' by noting it returns an array of notes/comments, not threaded discussions, and from 'gitlab_add_merge_request_comment' by being a read operation. This is specific and avoids tautology.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Use when: Reading MR discussions, reviews', providing clear context for when to invoke this tool. It also lists related tools with brief distinctions (e.g., 'gitlab_get_merge_request_discussions: Threaded discussions'), guiding the agent on alternatives. This offers explicit guidance on when to use this tool versus others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing authentication requirements ('uses authentication token'), privacy aspects ('private settings', 'not available via public APIs'), and the comprehensive nature of returned data. It doesn't mention rate limits or error conditions, but covers key behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Perfectly structured with clear sections: purpose statement, detailed return data breakdown, use cases, and parameter clarification. Every sentence adds value with zero waste, and the information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 0-parameter tool with no output schema, the description provides excellent context about what data is returned, use cases, and authentication. It could potentially mention response format or error handling, but given the tool's simplicity, it's quite complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0 parameters with 100% coverage, so baseline would be 3. The description adds value by explicitly stating 'No parameters required - uses authentication token', which clarifies the authentication mechanism beyond what the empty schema indicates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get', 'Retrieve') and resource ('current authenticated user's complete profile') with specific scope. It distinguishes from siblings like 'gitlab_get_user' by emphasizing it's for the authenticated user's own profile with private data not available via public APIs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'Use cases' section provides clear context for when to use this tool (dashboard display, account verification, statistics, data export). However, it doesn't explicitly state when NOT to use it or mention alternatives like 'gitlab_get_user' for other users' public profiles.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'essential user details like name, username, avatar, and public profile info,' which clarifies the scope and output. However, it doesn't mention rate limits, authentication requirements, or error conditions, leaving some behavioral aspects unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections: purpose, return values, usage guidelines, parameters, examples, and alternatives. Every sentence adds value without redundancy, and key information is front-loaded (e.g., purpose and when-to-use in the first two sentences).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a simple read operation with 2 parameters, the description is mostly complete. It covers purpose, usage, parameters, and alternatives effectively. However, it lacks details on output format or potential errors, which could be helpful for an agent, though not critical for this tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds value by clarifying the mutual exclusivity ('Use either user_id OR username, not both') and providing usage examples, but doesn't add significant semantic details beyond what's in the schema. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get basic profile information'), target resource ('for a specific GitLab user'), and method ('by ID or username'). It distinguishes from siblings like 'gitlab_search_user' by emphasizing exact identification versus partial search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use ('when you have a specific user ID or exact username and need basic profile information') and when not to use ('For searching users with partial information, use 'gitlab_search_user' instead'). It also mentions alternatives for more comprehensive data ('For comprehensive user activity and contributions, use user activity tools instead').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it's a read-only listing tool (implied by 'List'), returns paginated results (via per_page and page parameters), and includes details like commit info, MR context, and quality metrics. However, it doesn't explicitly mention rate limits, authentication needs, or error handling, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a clear purpose and usage guidelines, but it becomes verbose with repetitive sections like 'Use cases' and 'Returns merge-related commits with:' that overlap with earlier content. Sentences like 'Find all commits that originated from merge requests created by the specified user' are redundant, reducing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, 6 parameters with 100% schema coverage, and no output schema, the description is mostly complete. It covers purpose, usage, parameters, and behavioral aspects like return details. However, it lacks explicit information on output structure (e.g., format of returned data) and error cases, which would enhance completeness for a tool with no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds value by explaining parameter semantics beyond the schema: it clarifies that 'user_id' and 'username' are alternatives ('use either'), provides context for 'target_branch' (e.g., 'main'), and explains the purpose of 'include_review_metrics' and pagination parameters. However, it lists parameters like 'user_id' not in the schema, causing minor inconsistency.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List merge commits where a specific user performed the merge.' It specifies the verb ('list'), resource ('merge commits'), and scope ('where a specific user performed the merge'), and distinguishes it from sibling tools by explicitly contrasting with 'gitlab_get_user_commits' for commits authored by the user.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives: 'For all commits authored by user, use 'gitlab_get_user_commits' instead.' It also includes use cases like 'release management' and 'merge activity tracking,' and clarifies the tool's specific focus on merges performed by the user, not authored by them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it mentions pagination ('Yes (default 20 per page)'), filtering capabilities ('By date range, file path, branch'), and provides an example response structure. However, it doesn't cover aspects like rate limits or authentication needs, which could be relevant for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with key information (purpose, returns, usage, filtering, pagination), followed by an example and related tools. Every sentence adds value without redundancy, making it efficient and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 parameters, no output schema, no annotations), the description is quite complete: it covers purpose, usage, filtering, pagination, example response, and related tools. However, it could be more comprehensive by explicitly mentioning that it's a read-only operation or any error conditions, though the lack of annotations and output schema is partially mitigated by the detailed schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by briefly mentioning filtering ('Filtering: By date range, file path, branch') and pagination, but doesn't provide additional syntax or format details. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('List') and resource ('repository commits'), and it distinguishes itself from siblings by mentioning related tools like gitlab_get_commit and gitlab_search_in_project, which have different functions (detailed view vs. search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage guidance with 'Use when: Viewing history, finding specific changes', and it lists related tools with their purposes (e.g., gitlab_get_commit for full details, gitlab_search_in_project for search), clearly indicating when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: pagination ('Yes (default 50 per page)'), subgroup inclusion option, and the return format ('Array of projects'). However, it doesn't mention rate limits, authentication requirements, or error handling, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with key information (purpose, returns, usage, pagination, options). Each sentence serves a distinct purpose, and the example response and related tools sections add practical value without redundancy. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only listing tool with 5 parameters and no output schema, the description is quite complete. It covers purpose, usage, pagination, options, and provides an example response. However, without annotations, it could benefit from mentioning authentication or rate limits, though the example response format partially compensates for the lack of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 5 parameters. The description adds minimal value beyond the schema, mentioning only 'include_subgroups=true' as an option. It doesn't provide additional syntax, format details, or usage tips not already in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and resource 'projects within a group', making the purpose specific and unambiguous. It distinguishes from sibling tools like 'gitlab_list_projects' (general listing) and 'gitlab_get_project' (detailed view) by focusing on group-specific listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides 'Use when: Browsing group projects, finding projects in group hierarchy', giving clear context for when to apply this tool. It also lists related tools ('gitlab_get_group', 'gitlab_get_project') to guide users toward alternatives for different needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing pagination behavior ('Yes (default 50 per page)'), filtering capabilities ('By ownership, name search'), and example response structure. It doesn't mention authentication requirements or rate limits, but covers key operational behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with clear sections (Returns, Use when, Pagination, Filtering, Example response, Related tools), each providing essential information without redundancy. Every sentence serves a distinct purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only listing tool with 4 parameters and no output schema, the description provides comprehensive context including return format, usage scenarios, pagination details, filtering options, example response, and sibling tool relationships. The main gap is the lack of output schema, but the example response compensates reasonably.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 4 parameters. The description mentions filtering by ownership and name search, which aligns with the 'owned' and 'search' parameters, but doesn't add meaningful semantic value beyond what's in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('accessible GitLab groups'), and distinguishes it from siblings by specifying it returns an array of groups with specific fields. It explicitly differentiates from gitlab_get_group (full details) and gitlab_list_group_projects (projects within groups).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a dedicated 'Use when' section with explicit scenarios (browsing groups, finding group IDs, navigating hierarchy) and lists related tools with their specific purposes, providing clear guidance on when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it mentions filtering capabilities ('By state (opened/closed/all)'), pagination ('Yes (default 20 per page)'), and includes an example response structure. However, it doesn't cover potential rate limits, authentication requirements, or error conditions, leaving some behavioral aspects unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with key information (purpose, returns, usage, filtering, pagination), followed by an example and related tools. Every sentence earns its place by providing essential guidance without redundancy, making it highly efficient and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (list operation with filtering/pagination), no annotations, and no output schema, the description does a good job by covering purpose, usage, behaviors, and providing an example response. However, it lacks details on error handling or advanced filtering options (e.g., by labels or assignees), which could enhance completeness for a list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already fully documents all four parameters. The description adds minimal value beyond the schema by mentioning filtering by state and pagination, but doesn't provide additional syntax, format details, or usage nuances. This meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('List') and resource ('project issues'), distinguishing it from siblings like 'gitlab_get_issue' (for detailed view) and 'gitlab_search_in_project' (for content search). It explicitly mentions the return type ('Array of issues with details'), making the purpose unambiguous and well-differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with a 'Use when:' section ('Browsing issues, finding work items') and lists related tools with their specific purposes ('gitlab_get_issue: Get full issue details', 'gitlab_add_issue_comment: Comment on issue', 'gitlab_search_in_project: Search issue content'). This clearly indicates when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it specifies the return type ('Array of MRs with key information'), filtering capability ('By state'), pagination behavior ('Yes (default 20 per page)'), and provides an example response structure. It doesn't mention authentication requirements or rate limits, but covers most operational aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly structured with clear sections: purpose statement, return type, usage context, filtering, pagination, example response, and related tools. Every sentence earns its place with zero waste, and information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list operation with no output schema, the description provides excellent context: it specifies the return format, includes a detailed example response, explains filtering and pagination behavior, and relates to sibling tools. The only minor gap is lack of explicit mention about authentication or error handling, but overall it's highly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 4 parameters. The description mentions filtering by state and pagination, which aligns with parameters but doesn't add meaningful semantic value beyond what the schema provides. The baseline of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('project merge requests'), and distinguishes from siblings by specifying it returns an array with key information rather than full details or diffs. The opening line 'List project merge requests' is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides 'Use when: Reviewing MRs, finding specific MRs' and lists three related tools with their specific purposes ('Full MR details', 'View diffs', 'Merge an MR'), giving clear guidance on when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the return type ('Configured webhooks') and example response structure, which helps the agent understand the output. However, it lacks details on permissions, rate limits, or error handling, leaving some behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for purpose, returns, usage, shows, example, and related tools. Each sentence adds value without redundancy, and it's front-loaded with key information, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description compensates by providing an example response and usage context. It covers the tool's purpose and behavior adequately for a read-only list operation, though it could include more on error cases or pagination for full completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the single parameter. The description does not add any parameter-specific information beyond what the schema provides, such as clarifying the 'project_id' usage. Baseline 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('project webhooks'), making the purpose specific. It distinguishes from siblings by focusing on webhooks rather than other project entities like issues or merge requests, and explicitly names related tools for webhook creation and testing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes an explicit 'Use when' section ('Checking integrations'), providing clear context for when to use this tool. It also lists related tools ('gitlab_create_project_hook', 'gitlab_test_project_hook') as alternatives for different operations, offering comprehensive guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by specifying what's included in returns ('Assets, release notes, links') and clarifying the difference from tags. However, it doesn't mention pagination behavior, rate limits, authentication requirements, or error conditions that would be helpful for a list operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with clear sections: purpose statement, returns clarification, usage context, what's included, differentiation from tags, and related tools. Every sentence adds value with zero wasted words, making it easy for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list operation with no annotations and no output schema, the description provides good context about what releases contain and how they differ from tags. However, it doesn't describe the return format structure or mention pagination behavior (though parameters suggest pagination exists), leaving some gaps for a tool that returns potentially large result sets.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 5 parameters. The description doesn't add any parameter-specific information beyond what's in the schema, which meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('project releases'), specifying that it returns 'GitLab releases (not just tags)' which distinguishes it from the sibling tool gitlab_list_tags. The explicit distinction between releases and tags ('Releases have descriptions, assets; Tags are just git references') provides excellent differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes explicit 'Use when' guidance ('Finding versions, release notes') and provides clear alternatives with 'Related tools' section that names gitlab_list_tags and gitlab_create_release. This gives the agent specific context about when to use this tool versus other options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it's a write operation (implied by 'Merge'), returns a result with commit SHA, and lists prerequisites (no conflicts, approvals met, CI passing). It also mentions options like squash and auto-merge. However, it lacks details on error handling, rate limits, or authentication needs, which are important for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core action and return value, followed by usage guidelines, prerequisites, and related tools. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a merge operation with 7 parameters and no output schema, the description is mostly complete. It covers purpose, usage, prerequisites, and related tools, but lacks details on output format (beyond 'Merge result with commit SHA') and error scenarios. With no annotations, it compensates well but could be more thorough for a mutation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds minimal parameter semantics by mentioning 'Options: Squash, delete branch, auto-merge', which loosely maps to parameters like 'squash', 'should_remove_source_branch', and 'merge_when_pipeline_succeeds', but doesn't provide additional meaning beyond the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Merge an approved merge request') and resource ('merge request'), distinguishing it from siblings like gitlab_approve_merge_request (adds approval) and gitlab_rebase_merge_request (fixes conflicts). It explicitly mentions the return value ('Returns: Merge result with commit SHA'), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance with 'Use when: MR is approved and ready', 'Prerequisites:' listing conditions (no conflicts, approvals met, CI passing), and 'Related tools:' naming alternatives for checking status, adding approval, and fixing conflicts. This clearly defines when to use this tool versus others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the operation's purpose, prerequisites (fast-forward method, no conflicts, developer access), and what it fixes ('Out-of-date MR status'). It doesn't mention rate limits, error conditions, or detailed response format, but covers the essential behavioral context for this mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description uses a structured format with clear sections (Returns, Use when, Fixes, Requirements, Related tools) that makes information easy to parse. Every sentence earns its place with no wasted words, and the most critical information (the action) is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description provides good contextual completeness. It covers purpose, usage context, prerequisites, and related tools. The main gap is the lack of output format details (only mentions 'Rebase status' without elaboration), but given the structured guidance provided, it's mostly complete for agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing comprehensive documentation for both parameters. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting. No additional semantic context is provided for the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a clear verb+resource statement 'Rebase MR onto target branch' that specifies the exact action and target. It distinguishes from siblings like 'gitlab_merge_merge_request' by focusing on rebasing rather than merging, and from 'gitlab_get_merge_request' by being an action rather than a query.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit 'Use when' guidance ('MR is behind target branch'), lists prerequisites ('Requirements: Fast-forward merge method, No conflicts, Developer access'), and names related tools for checking if rebase is needed and for subsequent merging. This gives clear context for when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the tool is read-only ('No actual changes made'), validates inputs ('Validates file paths', 'Checks permissions'), and previews outcomes ('Shows: Affected files, potential errors'). However, it lacks details on error handling or response format, which would be beneficial for a tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, starting with the core purpose and return values. Each section ('Safety features', 'Related tools') adds value without redundancy. The bullet points enhance readability, and there is no wasted text—every sentence serves a clear purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, no output schema), the description does a good job of covering the tool's purpose, safety, and usage context. It explains what the tool returns and when to use it, which compensates for the lack of output schema. However, it could provide more detail on error scenarios or the structure of validation results to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description does not add any parameter-specific information beyond what the schema provides, such as explaining how parameters interact or their impact on the preview. This meets the baseline expectation when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Preview commit without creating') and distinguishes it from its sibling 'gitlab_create_commit'. It explicitly mentions what the tool does not do ('No actual changes made'), making its role distinct and well-defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('Use when: Validating before actual commit') and names a clear alternative ('gitlab_create_commit: Actual commit'). It also references another related tool for checking file existence, offering comprehensive context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the scope ('All public projects + your private projects'), the return type ('Projects matching search query'), and how it differs from siblings. However, it doesn't mention potential limitations like rate limits, authentication requirements, or pagination behavior (though pagination is hinted at via parameters). For a search tool with no annotations, this is good but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise, using bullet points and clear sections ('Returns:', 'Use when:', 'Scope:', 'Different from:', 'Related tools:'). Every sentence earns its place by providing essential information without redundancy. It's front-loaded with the core purpose and efficiently organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (a search function with 3 parameters), no annotations, and no output schema, the description does a good job of covering context: it explains purpose, usage, scope, and differences from siblings. However, it lacks details on output format (e.g., what fields are returned) and doesn't fully address behavioral aspects like error handling or authentication needs. It's mostly complete but has minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with detailed descriptions for each parameter (e.g., 'search' includes matching behavior and examples, 'per_page' and 'page' include ranges and defaults). The description adds no additional parameter semantics beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description, which applies here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search all GitLab projects' and 'Returns: Projects matching search query'. It specifies the verb ('search'), resource ('GitLab projects'), and scope ('all GitLab'), and explicitly distinguishes it from the sibling 'list_projects' tool by noting that this searches across all of GitLab while list_projects only shows accessible projects. This is specific and provides clear differentiation from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes explicit guidance: 'Use when: Finding projects across GitLab' and 'Scope: All public projects + your private projects'. It directly compares to 'list_projects' in a 'Different from' section and lists related tools ('gitlab_list_projects' and 'gitlab_search_in_project') with brief explanations of their differences. This provides clear when-to-use context and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it returns 'Structured diff with smart chunking', has 'Context control, size limits', and is 'Better for large diffs'. However, it doesn't mention potential rate limits, authentication needs, or error conditions, leaving some behavioral aspects uncovered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with clear sections (Returns, Use when, Features, Advantages, Related tools), uses bullet points for readability, and contains zero wasted sentences. Every element adds value, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description is quite complete: it explains purpose, usage context, advantages, and relationships to siblings. The main gap is the lack of output format details (what 'Structured diff' means exactly), but otherwise it provides strong contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, providing detailed documentation for all 5 parameters. The description adds minimal parameter semantics beyond the schema, only implying context lines and size limits through 'Features: Context control, size limits'. This meets the baseline of 3 since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Get intelligent diff between refs' with specific features like 'smart chunking', 'context control', and 'size limits'. It distinguishes itself from siblings by explicitly comparing to 'gitlab_get_commit_diff: Simple commit diff' and 'gitlab_compare_refs: Basic comparison', establishing its specialized role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with 'Use when: Need customizable diffs' and lists advantages over standard diff tools. It names specific alternatives ('gitlab_get_commit_diff', 'gitlab_compare_refs') and explains when this tool is better ('Better for large diffs', 'Configurable context lines', 'File size filtering').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It effectively describes what the tool returns ('Pipeline status and key findings'), its focus areas ('Failed jobs, error messages, duration'), and specific highlights ('Failed job names and stages, Error excerpts, Performance issues'). However, it doesn't mention potential limitations like rate limits or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Returns, Use when, Focus, Highlights, Related tools) and every sentence adds value. It's front-loaded with the core purpose and uses bullet points efficiently. No wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, 100% schema coverage, and no output schema, the description provides good context about what the tool does and when to use it. It explains the summary focus areas and relates it to sibling tools. The main gap is lack of output format details, but given the tool's purpose is clear and parameters are well-documented, this is a minor limitation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, providing comprehensive parameter documentation. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline of 3. The description's focus on summary content doesn't enhance parameter understanding beyond the schema's detailed descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Summarize CI/CD pipeline for AI' with specific focus on 'Pipeline status and key findings'. It distinguishes from siblings by mentioning related tools like gitlab_list_pipelines for finding pipelines and gitlab_get_pipeline_job_log for full logs, establishing its unique role in the pipeline analysis workflow.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Use when: Debugging CI failures with AI' and provides clear alternatives in the 'Related tools' section. This gives the agent specific guidance on when to use this tool versus other pipeline-related tools, with named alternatives for different use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: atomic execution ('All-or-nothing execution'), rollback behavior ('Automatic rollback'), and result referencing ('Reference previous operation results'). However, it lacks details on permissions, rate limits, or error handling beyond the stop_on_error parameter, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with bullet points and an example workflow, making it easy to scan. However, it includes some redundancy (e.g., repeating 'automatic rollback' in multiple sections) and could be more front-loaded; the first sentence is clear, but subsequent details could be tighter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 3 parameters, 100% schema coverage, and no output schema, the description does a good job covering usage, behavior, and examples. It addresses atomicity and chaining, but lacks output details (e.g., result format) and could better explain error scenarios, given the absence of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema documents parameters thoroughly. The description adds value by explaining the purpose of the 'operations' array ('Operation chaining', 'Result references') and contextualizing 'stop_on_error' in the example workflow, but doesn't significantly enhance parameter understanding beyond the schema's details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Execute multiple operations atomically') and distinguishes it from siblings by highlighting its batch nature. It explicitly contrasts with 'Individual operation tools' and positions itself for 'Complex multi-step workflows,' making its unique role evident.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('Use when: Complex multi-step workflows') and when not to (by referencing 'Individual operation tools' for simpler cases). It also mentions an alternative tool ('gitlab_safe_preview_commit: Test first') for testing, offering clear context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by specifying what it returns ('Full issue data including description, comments count'), providing a detailed example output, and explaining the IID parameter. However, it doesn't mention potential errors, rate limits, or authentication needs, leaving some behavioral aspects uncovered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (e.g., 'Returns:', 'Use when:', 'Required:', 'What's IID?:', 'Example:', 'Related tools:'), making it easy to scan. It's appropriately sized, though the detailed example output is lengthy but informative. Every sentence adds value, with no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is quite complete. It covers purpose, usage, parameters, and example output, but lacks details on error handling, authentication, or rate limits. The absence of an output schema is mitigated by the example return structure, though not fully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the baseline is 3. The description adds value by explaining the 'issue_iid' parameter in detail ('What's IID?: Internal ID - the issue number shown in GitLab') with examples, clarifying its semantics beyond the schema. However, it doesn't provide similar elaboration for 'project_id', which is auto-detected but could benefit from more context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get complete issue details') and resources ('issue'), and explicitly distinguishes it from sibling tools like 'gitlab_list_issues' (for finding issues) and 'gitlab_update_issue' (for modifying issues). The title 'Get complete issue details' reinforces this specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with a 'Use when:' section ('Need complete issue information'), lists required parameters ('Required: Issue IID'), and names related tools with their purposes (e.g., 'gitlab_list_issues: Find issues'). This clearly indicates when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well: it discloses pagination behavior ('Yes (default 20 per page)'), filtering capabilities ('By ownership, name search'), and provides a concrete example response. It doesn't mention authentication needs or rate limits, but covers key operational aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Excellent structure with clear sections: purpose, returns, usage, pagination, filtering, example, and related tools. Every sentence earns its place with zero waste. The information is front-loaded with the core purpose first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list tool with 100% schema coverage and no output schema, the description is complete: it explains what the tool does, when to use it, behavioral traits (pagination, filtering), provides example output, and distinguishes from siblings. No annotations exist, but the description compensates adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 4 parameters. The description mentions filtering 'By ownership, name search' which aligns with 'owned' and 'search' parameters, but adds no additional semantic value beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List accessible GitLab projects' with a specific verb ('List') and resource ('GitLab projects'), and distinguishes from siblings by mentioning 'gitlab_get_project' for full details and 'gitlab_search_projects' for broader search. It provides the exact return format and scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Use when: Browsing projects, finding project IDs' and lists related tools with clear distinctions: 'gitlab_get_project: Get full project details' and 'gitlab_search_projects: Search all GitLab projects'. This gives clear when-to-use guidance and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and excels. It discloses pagination behavior ('Yes (default 20 per page)'), response format details ('Array of jobs with status, timing, and artifact information'), and includes a concrete example response with key fields. This goes well beyond basic functionality to explain operational characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, returns, use when, pagination, details, example, related tools) and every sentence adds value. It's slightly longer than minimal but efficiently organized, with no redundant information. The example response is helpful but adds length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only listing tool with no annotations but 100% schema coverage, the description provides excellent context: clear purpose, usage scenarios, behavioral details (pagination, response format), example output, and related tools. It compensates for the lack of output schema by describing the return structure and providing an example.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all four parameters. The description doesn't add any parameter-specific information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'List jobs in a specific pipeline' with a clear verb ('List') and resource ('jobs'), distinguishing it from sibling tools like 'gitlab_list_pipelines' (which lists pipelines) and 'gitlab_list_project_jobs' (which lists all jobs in a project). The purpose is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a dedicated 'Use when:' section listing three specific scenarios (debugging pipeline failures, checking job status, finding artifacts), and a 'Related tools:' section explicitly names 'gitlab_list_pipelines' as a prerequisite for finding pipeline IDs and 'gitlab_download_job_artifact' as a follow-up. This provides clear when-to-use guidance and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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