mcp-gitlab-jira

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes targeting specific GitLab resources and actions, with clear boundaries like gitlab_create_issue vs. gitlab_update_issue. However, some overlap exists between gitlab_list_project_members and gitlab_list_project_members_by_project_name, which could cause confusion as they serve similar functions with minor differences in input parameters.
Naming Consistency5/5
All tools follow a consistent gitlab_verb_noun pattern with snake_case throughout, such as gitlab_get_issue_details and gitlab_trigger_pipeline. This predictability makes it easy for agents to understand and navigate the toolset without naming conflicts or style variations.
Tool Count3/5
With 32 tools, the count is borderline high for a GitLab-focused server, as it may feel heavy and potentially overwhelming for agents. While it covers many aspects, it approaches the upper limit where efficiency could be impacted, though it remains within a reasonable scope for comprehensive GitLab operations.
Completeness5/5
The toolset provides extensive coverage of GitLab's domain, including CRUD operations for issues, merge requests, branches, pipelines, and more, with no obvious gaps. It supports full lifecycle management from creation to updates and deletions, ensuring agents can handle most workflows without dead ends.
Average 3/5 across 32 of 32 tools scored.
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
- Last stable release on September 5, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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.
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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.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Gets jobs' but does not clarify if this is a read-only operation, what permissions are needed, whether it's paginated, or what the output format might be. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and constraints.
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 a single, efficient sentence with no wasted words, making it easy to parse. It is front-loaded with the core purpose, though it could benefit from more detail. This meets the criteria for conciseness and structure without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns complex job data. It does not address behavioral aspects like read-only nature, error handling, or output structure. With 100% schema coverage for inputs but no output information, the description fails to provide enough context for effective use.
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 both parameters ('projectPath' and 'pipelineId') documented in the schema. The description does not add any meaning beyond this, such as explaining parameter formats or relationships. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema handles the parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Gets jobs for a specific pipeline' clearly states the action ('Gets') and target resource ('jobs for a specific pipeline'), making the purpose understandable. However, it does not differentiate from sibling tools like 'gitlab_get_job_logs' or 'gitlab_get_pipeline_details', which could cause confusion about scope. It's not tautological but remains somewhat vague about what 'jobs' entails.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'gitlab_get_job_logs' (for logs) and 'gitlab_get_pipeline_details' (for pipeline metadata), there is no indication of context, prerequisites, or exclusions. Usage is implied only by the name, lacking explicit instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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. While 'Adds a comment' implies a write/mutation operation, it doesn't disclose important behavioral traits like required permissions, whether comments are editable/deletable, rate limits, or what happens on success/failure. This is inadequate for a mutation tool with zero 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 a single, efficient sentence with zero wasted words. It's appropriately sized for a straightforward tool and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 is insufficient. It doesn't explain what happens after the comment is added (success response, error conditions), nor does it provide context about permissions, limitations, or how this tool fits within the broader GitLab workflow.
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 adds no additional meaning about parameters beyond what's in the schema. This 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Adds a comment') and target resource ('to a GitLab issue'), providing a specific verb+resource combination. However, it doesn't differentiate from its sibling 'gitlab_add_comment_to_merge_request', which performs a similar action on a different resource type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites (like authentication), when not to use it, or how it differs from similar tools like 'gitlab_update_issue' which might also allow commenting.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Assigns' implies a mutation operation, the description lacks details on permissions required, whether assignments are additive or replace existing reviewers, error handling (e.g., invalid user IDs), or side effects. This is inadequate for a mutation tool with zero 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 a single, efficient sentence with zero waste—it directly states the tool's purpose without fluff or redundancy. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a mutation operation with 2 required parameters), lack of annotations, and no output schema, the description is incomplete. It fails to address behavioral aspects like permissions, idempotency, or response format, leaving significant gaps for an agent to operate safely and 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%, with both parameters ('mrUrl' and 'reviewerIds') clearly documented in the schema. The description adds no additional meaning beyond what the schema provides (e.g., format examples, sourcing of IDs). Baseline 3 is appropriate when the schema does the heavy lifting, though no extra value is added.
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 action ('Assigns reviewers') and target resource ('to a GitLab Merge Request'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'gitlab_update_issue' or 'gitlab_get_merge_request_details' which might also involve merge request operations, leaving room for ambiguity about when this specific assignment tool is appropriate versus alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing merge request access), exclusions (e.g., not for unassigned reviewers), or comparisons to siblings like 'gitlab_list_project_members' for finding user IDs. Without this context, an agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the action ('Cancels') but lacks critical details: whether this requires specific permissions, if the cancellation is reversible, what happens to associated jobs, or any rate limits. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
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 a single, direct sentence with zero wasted words. It's front-loaded with the core action and resource, making it highly efficient. This is an example of optimal conciseness for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permissions, side effects, or error conditions, nor does it explain what the tool returns. For a tool that alters system state, more context is needed to 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%, with both parameters ('projectPath' and 'pipelineId') clearly documented in the schema. The description adds no additional semantic context about these parameters, such as format examples or constraints. Baseline 3 is appropriate since the schema does the heavy lifting, but the description doesn't enhance understanding.
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 action ('Cancels') and resource ('a running pipeline'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'gitlab_retry_pipeline' or 'gitlab_trigger_pipeline' beyond the basic verb, missing an opportunity to clarify its specific role in pipeline management.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., pipeline must be running), exclusions (e.g., cannot cancel completed pipelines), or related tools like 'gitlab_retry_pipeline' for restarting after cancellation. This leaves the agent with minimal context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but provides minimal behavioral context. It states the action ('Closes') but doesn't disclose permissions needed, whether closing is reversible, what happens to associated data, or error conditions. This is inadequate for a mutation tool with zero 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 a single, efficient sentence with no wasted words. It's front-loaded with the core action, 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.
Completeness2/5
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 is incomplete. It lacks details on behavioral traits, error handling, return values, and differentiation from siblings, leaving significant gaps for an AI agent to use it correctly.
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 both parameters. The description adds no additional parameter information beyond what's in the schema, but this is acceptable given the high coverage, resulting in a baseline score of 3.
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 action ('Closes') and resource ('a GitLab issue'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'gitlab_update_issue' which might also close issues, or explain what 'closing' entails versus other states.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For example, it doesn't mention prerequisites (e.g., issue must be open), when not to use it (e.g., for deleting issues), or how it differs from 'gitlab_update_issue' which might also handle issue state changes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 action ('creates') but lacks critical details: whether this requires specific permissions, if it's idempotent (handling existing branches), rate limits, error conditions (e.g., invalid ref), or what happens on success (e.g., branch creation confirmation). For a mutation tool, this is a significant gap in transparency.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every part of the sentence earns its place by conveying essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 is incomplete. It doesn't address behavioral aspects like permissions, idempotency, or error handling, nor does it hint at return values (e.g., success confirmation or branch details). Given the complexity of creating a branch in a version control system, more context is needed for effective agent use.
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 input schema has 100% description coverage, clearly documenting all three required parameters (projectPath, branchName, ref). The description adds no additional semantic context beyond what's in the schema, such as formatting examples or constraints (e.g., branch naming conventions). With high schema coverage, a baseline score of 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('creates') and resource ('new branch in a GitLab project'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'gitlab_delete_branch' or 'gitlab_list_branches' beyond the basic verb, missing an opportunity to clarify its unique role in the toolset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing project access), compare it to similar tools like 'gitlab_list_branches' for checking existing branches, or specify use cases (e.g., feature development vs. hotfixes). This leaves the agent without 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.
Behavior2/5
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 but offers minimal information. It states this is a creation operation, implying mutation, but doesn't cover permissions needed, whether it's idempotent, rate limits, error conditions, or what happens on success (e.g., returns issue ID). For a mutation tool with zero annotation coverage, this is inadequate.
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 a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a basic tool definition and front-loads the essential information, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a mutation operation with 5 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like permissions, side effects, or return values, leaving significant gaps for an AI agent to understand how to use it effectively in context.
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 description adds no parameter-specific information beyond what's already in the schema (which has 100% coverage with clear descriptions for all 5 parameters). The baseline score of 3 reflects that the schema does the heavy lifting, and the description doesn't compensate with additional context like format examples or constraints.
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 action ('creates') and resource ('new issue in a GitLab project'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'gitlab_update_issue' or 'gitlab_close_issue', which would require specifying it's specifically for initial creation rather than modification or closure.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing project access), compare it to sibling tools like 'gitlab_update_issue' for modifications, or indicate when not to use it (e.g., for commenting instead use 'gitlab_add_comment_to_issue'). This leaves the agent without contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Deletes') but doesn't describe critical traits: whether deletion is permanent or reversible, what permissions are required, if it affects associated merge requests or pipelines, or what the response looks like (success/failure indicators). For a destructive operation with zero annotation coverage, this is a significant gap in transparency.
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 a single, direct sentence that efficiently conveys the core action without unnecessary words. It's front-loaded with the key verb ('Deletes') and resource. However, it could be slightly more structured by including a brief note on implications or prerequisites, but as-is, it earns its place without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's destructive nature, lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permanence, permissions, or error handling, nor does it explain return values. For a mutation tool with significant context gaps, the description should provide more guidance to ensure safe and correct 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%, with both parameters ('projectPath' and 'branchName') clearly documented in the schema. The description doesn't add any meaning beyond what the schema provides—it doesn't explain parameter formats, constraints, or examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 action ('Deletes') and resource ('a branch from a GitLab project'), making the purpose immediately understandable. It distinguishes from sibling tools like 'gitlab_create_branch' and 'gitlab_list_branches' by specifying deletion rather than creation or listing. However, it doesn't explicitly differentiate from other destructive operations like 'gitlab_close_issue' beyond the resource type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing appropriate permissions), when deletion is appropriate (e.g., after merging), or what happens to associated data. While siblings like 'gitlab_create_branch' and 'gitlab_get_branch_details' exist, no comparison or context is given to help choose between them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 this is a read operation ('Gets'), implying it's non-destructive, but doesn't mention authentication requirements, rate limits, error conditions, or what 'detailed information' includes (e.g., commit history, protection status). This leaves significant gaps for an agent to understand the tool's behavior.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'detailed information' returns (e.g., JSON structure, fields like last commit or merge status), which is critical for a tool with no output schema. For a read operation with 2 parameters, this leaves the agent guessing about the response format.
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 input schema has 100% description coverage, clearly documenting both required parameters ('projectPath' and 'branchName'). The description doesn't add any meaning beyond this, such as format examples or constraints, but with high schema coverage, a baseline score of 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Gets detailed information') and resource ('about a specific branch'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'gitlab_list_branches' or 'gitlab_get_file_content', which would require more specific language about what 'detailed information' entails.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'gitlab_list_branches' (for listing branches) or 'gitlab_get_file_content' (for branch-specific file details), nor does it specify prerequisites such as needing a specific branch name rather than exploring available branches first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 a read operation ('Gets'), which implies non-destructive behavior, but lacks details on permissions, rate limits, pagination, or response format. For a tool with zero annotation coverage, this is insufficient to inform the agent about how the tool behaves 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core purpose without unnecessary elaboration, earning full marks for brevity and clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for effective tool use. It doesn't cover behavioral aspects like authentication needs, error handling, or return values, which are critical for an agent to invoke the tool correctly. While concise, it fails to provide sufficient context beyond the basic action.
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 input schema has 100% description coverage, clearly documenting both parameters ('projectPath' and 'issueIid'). The description adds no additional semantic context about these parameters, such as format examples or constraints. Given the high schema coverage, the baseline score of 3 is appropriate as the schema adequately handles parameter documentation.
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 action ('Gets') and resource ('comments for a GitLab issue'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'gitlab_get_issue_details' or 'gitlab_get_merge_request_details' that also retrieve GitLab data, so it misses the highest score for sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention related tools like 'gitlab_get_issue_details' for issue metadata or 'gitlab_add_comment_to_issue' for adding comments, nor does it specify prerequisites such as needing an existing issue. This leaves the agent without 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Gets detailed information' but doesn't clarify what 'detailed' includes (e.g., fields like labels, assignees, or description), whether it's a read-only operation, authentication requirements, rate limits, or error handling. This leaves significant gaps for a tool that likely interacts with an external API.
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 a single, efficient sentence with zero wasted words. It front-loads the core purpose ('Gets detailed information') and specifies the resource, making it easy to parse. Every part of the sentence contributes directly to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool with external API interactions. It doesn't cover behavioral aspects like read-only nature, return format, error cases, or authentication needs. While the schema handles parameters well, the overall context for safe and effective use is insufficient.
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 clear parameter descriptions in the schema (e.g., 'The path of the GitLab project' for projectPath). The description adds no additional meaning beyond implying specificity ('about a specific GitLab issue'), which aligns with the schema but doesn't enhance it. 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 ('Gets') and resource ('detailed information about a specific GitLab issue'), making the purpose unambiguous. It distinguishes from siblings like gitlab_list_project_issues (which lists issues) and gitlab_get_issue_comments (which focuses on comments). However, it doesn't explicitly contrast with all siblings, such as gitlab_get_merge_request_details, which follows a similar pattern for a different resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing project access), contrast with gitlab_list_project_issues for browsing issues, or specify scenarios like retrieving issue metadata for updates. Usage is implied by the name but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 a read operation ('Gets'), implying it's non-destructive, but doesn't cover critical aspects like authentication requirements, rate limits, error conditions (e.g., invalid job ID), or output format (e.g., raw logs vs. structured data). For a tool with zero annotation coverage, this is insufficient.
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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and avoids unnecessary elaboration, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't address behavioral traits (e.g., authentication), usage context, or return values. For a tool that fetches operational data like logs, more context is needed to guide the agent 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%, with both parameters ('projectPath' and 'jobId') clearly documented in the schema. The description doesn't add any meaning beyond what the schema provides (e.g., format examples or relationship between parameters). 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Gets') and resource ('logs for a specific job'), making the purpose immediately understandable. However, it doesn't differentiate from potential sibling tools like 'gitlab_get_pipeline_jobs' or 'gitlab_get_pipeline_details' that might also provide job-related information, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing a valid job ID from another tool), context (e.g., after a pipeline run), or exclusions (e.g., not for archived jobs). This leaves the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Gets') but does not describe the return format (e.g., list of pipelines with statuses), pagination behavior, error conditions, or authentication requirements. This leaves significant gaps for an agent to understand how to handle the tool's output and potential issues.
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 a single, clear sentence with no wasted words. It is front-loaded with the core action and resource, making it easy to parse quickly. Every part of the sentence contributes directly to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is insufficient for a tool that retrieves data. It does not explain what the output contains (e.g., pipeline details, statuses, or IDs), how results are structured, or any behavioral nuances like rate limits or error handling. This leaves the agent with incomplete information 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%, with the single parameter 'mrUrl' documented as 'The URL of the GitLab Merge Request.' The description does not add any additional meaning beyond this, such as URL format examples or validation rules. With high schema coverage, the baseline score of 3 is appropriate as the schema adequately covers parameter semantics.
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 ('Gets') and resource ('pipelines for a specific GitLab Merge Request'), making the purpose unambiguous. However, it does not differentiate from sibling tools like 'gitlab_get_pipeline_details' or 'gitlab_get_project_pipelines', which also retrieve pipeline-related data but for different scopes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, such as needing a valid merge request URL, or compare it to similar tools like 'gitlab_get_pipeline_details' for individual pipelines or 'gitlab_get_project_pipelines' for broader pipeline listings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 tool 'Gets pipelines' but lacks details on permissions required, rate limits, pagination, return format, or whether it's read-only (implied but not explicit). This is a significant gap for a tool that likely interacts with an external API, making it inadequate for safe and effective use.
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 a single, efficient sentence that front-loads the core purpose and includes the optional filtering detail. There is no wasted text, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a GitLab API tool with no annotations and no output schema, the description is incomplete. It fails to address key aspects like authentication needs, error handling, response format, or pagination, which are critical for an agent to use this tool correctly in a real-world scenario.
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 parameters (projectPath and ref). The description adds minimal value by mentioning optional filtering by branch/ref, which is already clear from the schema. No additional semantics or examples are provided, resulting in a baseline score of 3.
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 ('Gets') and resource ('pipelines for a GitLab project'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like 'gitlab_get_pipeline_details' or 'gitlab_get_merge_request_pipelines', which might retrieve similar data in different contexts, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as 'gitlab_get_pipeline_details' for specific pipeline info or 'gitlab_get_merge_request_pipelines' for MR-related pipelines. It mentions optional filtering by branch/ref but does not clarify use cases or exclusions, leaving the agent to infer usage from context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 it 'fetches activities' but does not disclose behavioral traits like whether it requires authentication, rate limits, pagination, or what the return format looks like. This is a significant gap 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 a single, efficient sentence that front-loads the core purpose and includes the optional filter. There is no wasted text, making it appropriately sized and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 is incomplete. It lacks details on behavioral aspects (e.g., authentication, response format) and does not compensate for the missing structured data, making it inadequate for a tool that fetches user data.
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 fully. The description adds minimal value by mentioning optional date filtering but does not provide additional semantic context beyond what's in the schema, 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'fetches' and the resource 'activities for a given GitLab user', making the purpose specific and understandable. However, it does not explicitly differentiate from siblings like 'gitlab_get_user_id_by_username', which might be a related lookup, so it misses full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as other user-related or activity-related tools in the sibling list. It mentions optional date filtering but does not specify use cases, prerequisites, or exclusions, leaving usage context vague.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 action ('fetches') but does not mention key traits like read-only nature, pagination, error handling, or authentication requirements, leaving significant gaps for a tool that likely interacts with an external API.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized, making it easy to understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is insufficient for a tool that likely returns a list of releases. It does not explain the return format, error conditions, or behavioral aspects, leaving the agent with incomplete information for proper invocation and 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 input schema fully documents the single parameter 'projectPath'. The description adds no additional meaning beyond the schema, such as examples or constraints, but the baseline score of 3 is appropriate since the schema handles the parameter documentation adequately.
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 action ('fetches') and resource ('releases for a given GitLab project'), making the purpose understandable. However, it does not differentiate from the sibling tool 'gitlab_list_releases_since_version', which suggests a more specific scope, leaving room for improvement in sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as the sibling 'gitlab_list_releases_since_version' for filtered releases. It lacks context on prerequisites or exclusions, offering only basic usage without comparative advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states the action ('Lists') but lacks details on output format (e.g., list of branch names, pagination, sorting), permissions required, rate limits, or error conditions. For a read operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 a single, direct sentence with zero waste—it states the core action and resource without fluff. It's appropriately sized for a simple list operation and front-loaded with essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 input schema, the description is incomplete. It doesn't explain what the tool returns (e.g., branch names, details), handling of large result sets, or authentication needs. For a tool that lists data, this lack of output context is a significant gap, making it inadequate for full 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%, with the parameter 'projectPath' documented in the schema. The description adds no additional meaning beyond implying the tool operates on a project, which is already clear from the schema. Baseline is 3 since the schema adequately covers parameter semantics without extra value from the description.
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 action ('Lists') and resource ('all branches in a GitLab project'), making the purpose immediately understandable. It distinguishes from siblings like gitlab_get_branch_details (which retrieves details for a specific branch) and gitlab_create_branch/gitlab_delete_branch (which modify branches). However, it doesn't explicitly mention the scope (e.g., all branches vs. filtered) beyond 'all', which slightly limits differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing project access), contrast with gitlab_get_branch_details for single-branch info, or suggest scenarios like exploring project structure. Usage is implied by the name but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Lists') but doesn't describe any behavioral traits such as pagination, sorting, filtering options, rate limits, authentication needs, or what the output looks like (e.g., list format, fields included). This leaves significant gaps for a tool that likely returns multiple items.
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 a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It's front-loaded and appropriately sized for a simple list operation, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a list tool. It doesn't explain what the output includes (e.g., MR titles, IDs, states), how results are limited or paginated, or any error conditions. For a tool that likely returns structured data, this leaves too much undefined for effective agent use.
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 input schema has 100% description coverage, with the single parameter 'projectPath' clearly documented in the schema. The description doesn't add any meaning beyond what the schema provides, such as examples of valid project paths or constraints. Since schema coverage is high, the baseline score of 3 is appropriate.
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 ('Lists') and resource ('merge requests for a given GitLab project'), making the purpose specific and understandable. However, it doesn't distinguish this tool from sibling tools like 'gitlab_list_project_issues' or 'gitlab_list_branches' beyond the resource type, missing explicit differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'gitlab_get_merge_request_details' for specific MRs or 'gitlab_list_all_projects' for broader context, leaving the agent to infer usage based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 tool lists issues but doesn't describe key behaviors: whether it requires authentication, returns paginated results, includes all issue fields or a subset, or has rate limits. For a read operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves in practice.
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 a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose ('Lists issues in a GitLab project'), making it immediately scannable. Every word earns its place, and there's no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (listing issues with filtering), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects (e.g., pagination, authentication needs) or output format (e.g., what fields are returned). For a tool with two parameters and no structured output documentation, the description should provide more context to be fully helpful.
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 input schema has 100% description coverage, clearly documenting both parameters ('projectPath' and 'state' with enum values). The description adds no parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even without param details 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 action ('Lists') and resource ('issues in a GitLab project'), making the purpose immediately understandable. It distinguishes this tool from siblings like 'gitlab_get_issue_details' (which retrieves a single issue) and 'gitlab_create_issue' (which creates issues). However, it doesn't specify the scope (e.g., all issues vs. filtered) or differentiate from 'gitlab_list_all_projects' (which lists projects, not issues).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose it over 'gitlab_get_issue_details' for specific issues or 'gitlab_list_all_projects' for project-level operations. There's no context about prerequisites (e.g., needing project access) or typical use cases (e.g., triaging open issues).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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. While it correctly describes a read operation ('Lists'), it provides no information about permissions required, pagination behavior, rate limits, error conditions, or what the output format looks like. For a tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a simple list operation and gets straight to the point with no unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 is insufficiently complete. It doesn't explain why an MR URL is needed to list project members (rather than a project identifier), what the output format will be, or any behavioral characteristics. The combination of missing structured data and minimal description leaves significant gaps for an agent trying to use this 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?
The schema has 100% description coverage, with the single parameter 'mrUrl' clearly documented as 'The URL of a GitLab Merge Request within the project.' The description adds no additional parameter information beyond what's already in the schema, which is acceptable given the high schema coverage. The baseline score of 3 reflects adequate but minimal value addition.
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 action ('Lists') and resource ('all members of a given GitLab project'), making the purpose immediately understandable. However, it doesn't differentiate from the sibling tool 'gitlab_list_project_members_by_project_name', which appears to serve a similar function but uses a different parameter approach.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't explain why one would use this tool (which requires an MR URL) instead of 'gitlab_list_project_members_by_project_name' (which presumably uses a project name), nor does it mention any prerequisites or constraints for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states it lists members without detailing behavioral traits like pagination, rate limits, authentication needs, or output format. It mentions 'all members' but doesn't clarify if this includes inactive users or specific roles, leaving gaps in transparency.
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 a single, efficient sentence with zero waste, front-loaded with the core action. It avoids redundancy and is appropriately sized for a simple list operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 is incomplete for a tool that likely returns a list of members. It doesn't explain return values, error handling, or important behavioral aspects like access controls, making it inadequate 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 the 'projectName' parameter. The description adds minimal value by reiterating 'by project name' but doesn't provide additional semantics like format examples or constraints beyond what the schema offers.
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 ('Lists') and resource ('members of a given GitLab project'), specifying it's by project name. It distinguishes from sibling 'gitlab_list_project_members' by explicitly mentioning 'by project name', though both likely serve similar purposes with different parameters.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'gitlab_list_project_members' or 'gitlab_get_user_activities' is provided. The description implies usage for listing project members but offers no context on prerequisites, permissions, or comparison with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 but doesn't describe key traits such as authentication requirements, rate limits, pagination behavior, error handling, or what the output looks like (e.g., list format, fields included). This is a significant gap for a tool that interacts with an external API.
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 a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action and parameters, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of interacting with GitLab's API, no annotations, and no output schema, the description is insufficient. It lacks details on behavioral aspects (e.g., auth, errors), output format, and usage context, leaving the agent poorly equipped to use the tool effectively in real 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 input schema already documents both parameters thoroughly. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't clarify version format constraints or project matching behavior), resulting in a baseline score of 3.
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 a specific verb ('filters') and resource ('releases for a given GitLab project'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'gitlab_list_all_releases', which might cause confusion about when to use each.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'gitlab_list_all_releases' or other filtering methods. It mentions the filtering capability but doesn't specify scenarios, prerequisites, or exclusions, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 action ('retries') but doesn't explain what 'retry' entails (e.g., re-running all jobs, resetting status, permissions required, side effects, or error handling). This is a significant gap for a mutation tool with zero 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 a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a mutation tool (retrying a pipeline) with no annotations and no output schema, the description is incomplete. It lacks details on behavior, outcomes, error conditions, or how it differs from sibling tools, making it inadequate for safe and effective use by an AI agent.
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 clear descriptions for both parameters ('projectPath' and 'pipelineId'). The description adds no additional parameter semantics beyond what the schema provides, so the baseline score of 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('retries') and target resource ('a failed pipeline'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'gitlab_cancel_pipeline' or 'gitlab_trigger_pipeline' in terms of when to use each, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., that the pipeline must be in a failed state), contrast it with 'gitlab_cancel_pipeline' or 'gitlab_trigger_pipeline', or specify any constraints, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states the action ('Triggers') implying a write operation, but doesn't disclose permissions needed, rate limits, side effects (e.g., starting CI/CD jobs), or what happens on failure. This leaves significant 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 a single, direct sentence with zero wasted words. It's front-loaded with the core action and resource, making it highly efficient and scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 is insufficient. It doesn't explain what the tool returns (e.g., pipeline ID, status), error conditions, or behavioral nuances like how variables affect pipeline execution. More context is needed for safe and effective use.
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 parameters are well-documented in the schema itself. The description adds no additional meaning about parameters beyond implying 'branch/ref' usage, which is already covered. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Triggers') and resource ('new pipeline for a specific branch/ref'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like gitlab_retry_pipeline or gitlab_cancel_pipeline, which also operate on pipelines but with different intents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid project path or branch), nor does it contrast with related tools like gitlab_retry_pipeline (for existing pipelines) or gitlab_get_project_pipelines (for viewing).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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. 'Updates' implies mutation but doesn't specify required permissions, whether changes are reversible, error handling, or what happens to fields not mentioned in updates. For a mutation tool with zero annotation coverage, this is a significant gap.
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 a single, efficient sentence that states the core purpose without any wasted words. It's appropriately sized and front-loaded with the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 is insufficient. It doesn't explain what the tool returns, error conditions, authentication requirements, or how it differs from sibling tools. The description should provide more context given the complexity of updating issues in GitLab.
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 no additional parameter information beyond what's in the schema. According to scoring rules, 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Updates') and resource ('an existing GitLab issue'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'gitlab_close_issue', which could also be considered an issue update operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (like needing an existing issue), doesn't contrast with 'gitlab_close_issue' for state changes, and offers no context about when this general update tool is preferable to more specific tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 action ('fetches') but lacks details on permissions required, rate limits, error handling, or the format of the returned content (e.g., plain text, binary). This is a significant gap for a tool that interacts with a version control system.
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 a single, well-structured sentence that efficiently conveys the core functionality without any fluff. It's front-loaded with the key action and resource, making it easy to parse quickly.
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 (3 required parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks behavioral details and usage context, which are important for a file-fetching operation in GitLab. The high schema coverage helps offset some 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?
The input schema has 100% description coverage, clearly documenting each parameter's purpose. The description adds no additional semantic context beyond what's in the schema (e.g., it doesn't explain how 'mrUrl' relates to 'filePath' or 'sha', or provide examples). 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 action ('fetches the content') and resource ('specific file at a given SHA in a GitLab project'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from potential siblings like 'gitlab_get_branch_details' or 'gitlab_get_merge_request_details' that might also retrieve content, though the focus on file content is reasonably distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. For example, it doesn't mention if this is for retrieving raw file content versus metadata, or how it differs from other file-related operations that might exist in GitLab. This leaves the agent to infer usage from the name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'including file diffs,' which adds some context about returned data, but lacks critical details like authentication requirements, rate limits, error handling, or whether it's a read-only operation. This is inadequate for a tool with potential complexity.
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 a single, efficient sentence that front-loads the core purpose ('fetches detailed information') and adds a useful detail ('including file diffs') without unnecessary words. Every part earns its place, making it highly concise and well-structured.
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 is minimally complete for a read operation but lacks depth. It hints at returned data ('file diffs') but omits other behavioral aspects like permissions or response format. For a tool fetching detailed MR info, more context would be beneficial to fully guide an agent.
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 input schema has 100% description coverage, clearly documenting the single parameter 'mrUrl.' The description does not add any semantic details beyond what the schema provides, such as URL format examples or validation rules, so it 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 ('fetches') and resource ('detailed information about a GitLab Merge Request'), making the purpose evident. It distinguishes from siblings like 'gitlab_list_merge_requests' by specifying it retrieves details for a single MR, but does not explicitly contrast with other detail-fetching tools like 'gitlab_get_issue_details'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It does not mention prerequisites (e.g., needing a specific MR URL) or compare with similar tools like 'gitlab_get_merge_request_pipelines' for pipeline-related details, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 this is a read operation ('Gets'), implying it's non-destructive, but doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or what specific details are returned (e.g., status, duration, artifacts). This leaves significant gaps for an agent to understand how to use it effectively.
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 a single, clear sentence with no wasted words. It's front-loaded with the core purpose, 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.
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 (a read operation with 2 parameters) and no output schema, the description is minimally adequate but incomplete. It lacks details on return values, error cases, or behavioral context, which are crucial for an agent to use it correctly without annotations to compensate.
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 ('projectPath' and 'pipelineId') adequately. The description doesn't add any meaning beyond this, such as format examples for 'projectPath' or how to obtain 'pipelineId'. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Gets') and resource ('detailed information about a specific pipeline'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'gitlab_get_pipeline_jobs' or 'gitlab_get_project_pipelines', which also retrieve pipeline-related information but with different scopes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For example, it doesn't clarify that this retrieves details for a single pipeline by ID, unlike 'gitlab_get_project_pipelines' which lists multiple pipelines or 'gitlab_get_pipeline_jobs' which focuses on jobs within a pipeline.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 ('retrieves') but lacks details on permissions required, rate limits, error handling, or output format. For a lookup tool with zero annotation coverage, this leaves significant 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 a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it easy to parse. Every part of the sentence earns its place by conveying 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 low complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage, behavior, and output, which are needed for a complete understanding. The schema compensates for parameters, but other gaps remain.
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 'username' parameter fully. The description adds no additional meaning beyond implying it maps to a GitLab user, which is redundant with the schema. Baseline 3 is appropriate as the schema handles parameter documentation.
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 ('retrieves') and resource ('GitLab user ID'), making the purpose unambiguous. It distinguishes from siblings by focusing on user ID lookup rather than project, issue, or pipeline operations. However, it doesn't explicitly differentiate from potential user-related siblings like 'gitlab_get_user_activities', though that tool has a different scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a username), exclusions, or related tools for user management. The agent must infer usage from the description alone without explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'fuzzy, case-insensitive match,' which adds some context beyond the input schema, but it does not cover critical aspects like pagination, rate limits, authentication requirements, error handling, or the format of returned results. For a tool with no annotations, this leaves significant 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 a single, efficient sentence that front-loads the key information: the action, resource, and filtering method. There is no wasted text, and it is 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.
Completeness3/5
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 output schema, no annotations), the description is minimally adequate. It covers the basic purpose and filtering behavior but lacks details on usage guidelines, behavioral traits, and output format. Without annotations or an output schema, more context would be beneficial, but it meets the minimum for a simple 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 input schema has 100% description coverage, with the parameter 'projectName' documented as 'The name or partial name of the project to filter by.' The description adds value by specifying 'fuzzy, case-insensitive match,' which clarifies the matching behavior beyond the schema. However, since schema coverage is high, the baseline is 3, and the description provides only marginal additional semantics.
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: 'Filters GitLab projects by name using a fuzzy, case-insensitive match.' It specifies the action (filters), resource (GitLab projects), and key constraint (by name with fuzzy, case-insensitive matching). However, it does not explicitly differentiate from its sibling 'gitlab_list_all_projects', which is a notable gap in sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention the sibling 'gitlab_list_all_projects' or specify scenarios where fuzzy name filtering is preferred over listing all projects or other filtering methods. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions the action ('adds a comment') but doesn't disclose critical behavioral traits such as required permissions, whether comments are editable/deletable, rate limits, or error conditions. The description is functional but lacks operational context needed for safe invocation.
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 a single, well-structured sentence that efficiently conveys the tool's capabilities without redundancy. It front-loads the core purpose and then enumerates the three comment types, with every word earning its place.
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 (4 parameters with nested objects, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameter mapping but lacks completeness regarding behavioral traits, error handling, and output expectations, which are important for a mutation tool with no structured safety annotations.
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 marginal value by hinting at the three use cases (general, reply, inline) which map to the optional 'discussionId' and 'position' parameters, but doesn't provide additional syntax or format details beyond what the schema provides.
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 ('adds a comment') and resource ('to a GitLab Merge Request'), making the purpose unambiguous. It distinguishes from sibling tools like 'gitlab_add_comment_to_issue' by specifying the target is a merge request, not an issue. However, it doesn't explicitly differentiate from other merge request tools beyond the comment action.
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 implies usage context by listing three comment types (general, reply, inline), suggesting when this tool is appropriate. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'gitlab_update_issue' for issue comments or other merge request tools, nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 lists 'all accessible' projects, implying scope, but lacks details on permissions, pagination, rate limits, or output format. For a list operation with zero annotation coverage, this leaves significant 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 two sentences with zero waste: the first states the purpose, and the second provides crucial usage guidance. It is front-loaded and efficiently structured.
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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but could be more complete. It covers purpose and guidelines well but lacks behavioral details like output structure or limitations, which would help an agent use 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?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info, which is appropriate, but it could have mentioned implicit aspects like authentication or filters. Baseline is 4 for zero parameters.
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 ('Lists') and resource ('all accessible GitLab projects'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'gitlab_list_projects_by_name' beyond mentioning it as an alternative, which slightly reduces 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 explicitly provides usage guidance by stating 'Try to use list_projects_by_name as it is more efficient,' which clearly indicates when to prefer an alternative tool. This is a direct and helpful comparison to a sibling tool.
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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