mcp-gitlab-workflow

Server Quality Checklist

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Latest release: v0.1.2
Disambiguation4/5
Most tools have distinct purposes targeting specific GitLab resources (issues, MRs, labels, files). A few potential overlaps exist (e.g., gitlab_add_issue_comment vs gitlab_create_mr_note) but they apply to different resources, and descriptions clarify the difference.
Naming Consistency3/5
Tools use two prefixes ('gitlab_' and 'workflow_'), creating a split. Within each prefix, naming follows verb_noun pattern, but verbs are inconsistent (e.g., 'add' vs 'create', 'get' vs 'list'). Overall pattern is present but not uniform.
Tool Count4/5
28 tools cover both low-level GitLab API actions and high-level workflow orchestrations, which is appropriate for a full-featured workflow server. While on the higher side, each tool serves a distinct role.
Completeness3/5
Core operations for issues, MRs, labels, and files are covered, but notable gaps exist: no tool to list issues, no update issue tool, and no delete for issues/MRs. The high-level workflows partially compensate but leave some CRUD incomplete.
Average 3.5/5 across 28 of 28 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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. It only states the action without disclosing any behavioral traits such as side effects, authentication needs, or data mutations. For example, it does not indicate whether adding a note triggers notifications or requires specific permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it lacks structure. Important information about usage and behavior is missing, making it too sparse for a tool with three parameters and no annotations.
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?
The tool has a simple purpose but the description is incomplete. It does not explain what a note/comment is, the format of the body, any constraints (e.g., markdown support), or the return value. With no annotations and an output schema present, the description should provide more 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 input schema has 100% description coverage for all three parameters (project_id, mr_iid, body), with clear explanations. The description does not add any additional semantic meaning beyond what the schema provides, so 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 tool creates a merge-request note or comment. The verb 'create' and resource 'merge-request note' are specific, distinguishing it from issue comment tools like gitlab_add_issue_comment. However, it lacks additional context that could further differentiate it from related tools like gitlab_get_mr_notes.
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 such as gitlab_add_issue_comment or gitlab_approve_mr. The description does not mention use cases, prerequisites, or when not to use it.
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?
Without annotations, the description carries full burden for behavioral disclosure. It only states a read operation (get notes), but does not disclose pagination, rate limits, authentication needs, error modes, or response structure. The description is minimal and leaves important behavioral traits unspecified.
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 sentence that is concise and includes the endpoint path for clarity. It is front-loaded and contains no extraneous information. However, it could be slightly more structured to improve readability.
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 has 4 parameters and an output schema, the description is minimal. It does not reference the output schema or explain what the returned notes contain. It lacks information about filtering, sorting, or potential edge cases, making it adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond what the input schema already provides for each parameter. It does not elaborate on parameter values or usage tips.
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 retrieves merge request notes/comments and includes the API endpoint path. It is specific about the resource and action, but does not explicitly differentiate from sibling tools like gitlab_get_mr_changes or gitlab_create_mr_note.
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. There is no mention of when not to use it, prerequisites, or recommendations for sibling tools that might be more appropriate in certain scenarios.
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 provided, so the description must disclose behavioral traits. It only states the basic action without mentioning idempotency, side effects of approving an already approved MR, authentication requirements, or whether approval is reversible.
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?
Single sentence, highly concise. However, it could briefly mention prerequisites or refer to parameters without losing compactness.
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?
Despite having an output schema, the description lacks essential context like prerequisites, permission requirements, or behavior when the MR is already approved. The tool has 3 parameters, but the description does not explain their role or the tool's overall 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 coverage is 100%, providing detailed descriptions for all parameters, especially project_id. The tool description adds no additional parameter context, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action (Approve) and the resource (merge request) with the method (GitLab approval API), making the purpose very clear. It distinguishes from the sibling tool 'gitlab_unapprove_mr' which reverses the action.
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, prerequisites (e.g., MR must be open, user needs approval permissions), or when to avoid it. Without context, the 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 behavioral burden but fails to disclose traits like error handling (e.g., branch already exists), permissions needed, or mutability. The terse description 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with the verb front-loaded. It is concise but could include additional behavioral context without harming conciseness.
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?
Although an output schema exists and schema coverage is high, the description omits important contextual details (e.g., idempotency, ref validity). The minimalism leaves gaps for 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?
Input schema provides 100% description coverage for all three parameters, including detailed guidance on project_id. The tool description adds no extra meaning beyond the schema, meeting baseline but not exceeding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Create repository branch by GitLab REST API' clearly states a specific verb (create) and resource (repository branch), distinguishing it from sibling tools like gitlab_create_issue or gitlab_create_merge_request.
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 (e.g., gitlab_commit_files) or what prerequisites are needed. The description lacks 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 present, so the description must disclose behavioral traits. It only states the API endpoint, omitting details about idempotency, side effects (e.g., triggering pipelines), authentication, or error conditions. The input schema provides some constraints, but the tool description itself lacks behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the core action. It is efficient and wastes no words, though it could benefit from slightly more depth.
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 14 parameters, no annotations, and the presence of an output schema, the description is far too minimal. It does not explain the overall workflow, return values, or how the tool fits into a larger process. The input schema compensates somewhat, but the description lacks 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?
Schema description coverage is 100%, so the baseline is 3. The tool description adds no extra meaning beyond the schema; the parameter descriptions in the schema are detailed (e.g., for project_id, referencing runtime config), but the top-level description does not enhance them.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Create' and resource 'merge request', and explicitly references the GitLab REST API endpoint. This distinguishes it from sibling tools like gitlab_get_merge_request and gitlab_approve_mr.
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 (e.g., when to create a merge request vs. approving or getting one). No context about prerequisites or when not to use it is given.
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 present, so the description carries full burden. It only states the action and API path, without disclosing side effects (e.g., permanence, permission requirements, or impact on issues). This is insufficient for an agent to understand behavioral implications.
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, short sentence that efficiently conveys the core action without extraneous words. It is well front-loaded.
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 deletion action and absence of annotations, the description lacks important context such as idempotency, error conditions, or whether deletion is reversible. Although an output schema exists (not shown), the description does not address the tool's overall completeness for 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?
Schema coverage is 100% with detailed parameter descriptions, including fallback logic for project_id. The tool description adds no extra semantic meaning beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Delete') and resource ('project label'), and includes the API endpoint for additional clarity. Among sibling tools like gitlab_create_label and gitlab_update_label, the purpose is unmistakable.
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 does not provide explicit guidance on when to use this tool versus alternatives. There is no mention of appropriate contexts, prerequisites, or situations where this tool should be avoided. The usage is only implied by the action name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. 'Get' implies a read operation, but it does not explicitly state idempotency, safety (read-only), error handling, or authentication needs. Adequate but not thorough.
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?
A single sentence, no redundancy or fluff. However, it is overly brief and could expand on usage or behavior without harming conciseness.
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 simplicity and presence of an output schema (which explains return values), the description is still too minimal. It lacks context about runtime config for project_id, branch fallback behavior, and what constitutes a successful response.
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?
Input schema covers 100% of parameters (project_id, file_path, ref). The description adds the API endpoint context but does not enhance parameter meaning beyond the schema descriptions. Baseline 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?
Description clearly states verb 'Get' and resource 'repository file', along with the API endpoint. This is specific, but does not explicitly distinguish from sibling tools like gitlab_upload_project_file, which also deals with files.
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 (e.g., gitlab_commit_files for writing, or other read tools). No conditions or exclusions are mentioned.
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. It does not disclose side effects (e.g., file modification, permissions needed, rate limits, or error conditions). The return metadata is implied but not detailed; output schema partially covers return format but behavioral aspects are missing.
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 concise sentence that communicates the core purpose. It is efficient with no redundant words, though additional structure (e.g., bullet points for output) could be beneficial.
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 4 parameters and an output schema, the description lacks context about when to use it, behavioral expectations, and error handling. It is minimal and leaves gaps for the agent to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with detailed descriptions for each parameter (especially project_id). The description does not add extra semantics beyond the schema; baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (upload binary file), target (project markdown uploads), and what is returned (markdown/url metadata). It distinguishes from siblings like gitlab_get_file or gitlab_commit_files which handle different operations.
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. It does not mention prerequisites, when not to use it, or suggest other tools for different purposes. The description only states functionality without 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 full burden. It states the tool creates a commit, which implies a mutation, but it does not disclose any behavioral traits such as required permissions, rate limits, idempotency, or side effects (e.g., whether it pushes the commit immediately). The description is too brief to adequately inform an AI agent about 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the main action. However, it is so brief that it may sacrifice clarity on usage context. Still, there is no fluff, and it earns a high score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters including nested objects and an output schema, the description is minimal. It provides no context beyond the basic action, such as when to use multiple actions, error scenarios, or integration with workflows. The existence of an output schema reduces the burden for return values, but additional context is needed for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, providing detailed semantics for each parameter (e.g., project_id includes runtime config instructions). The tool description itself adds no additional meaning beyond the schema, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates one commit with multiple file actions via the GitLab commits API. It specifies the verb 'create' and the resource 'one commit with multiple file actions', which distinguishes it from sibling tools like gitlab_create_branch or gitlab_get_file that do not perform commits.
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 does not provide any guidance on when to use this tool versus alternatives. It does not mention when not to use it, prerequisites (e.g., branch existence), or compare to potential alternatives like creating separate commits. The usage context is only implied by the purpose.
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 must disclose behavioral traits. It only mentions the endpoint and does not describe pagination, rate limits, or whether the operation is read-only (though implied).
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, concise sentence that states the purpose without fluff. However, it could be slightly more informative without losing brevity.
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 has 6 parameters and an output schema is present, the description could explain pagination behavior or search functionality. The existing description is adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds no parameter-specific information beyond the schema's own descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and the resource 'labels of a GitLab project', with the endpoint path. This distinguishes it from sibling tools like gitlab_create_label (create) and gitlab_delete_label (delete).
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 such as gitlab_create_label or gitlab_update_label. The description only states the action, leaving the agent to infer usage 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 provided, and the description does not disclose mutation behavior, required fields for identification, or error handling; it only states the action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise single sentence, but could marginally include usage conditions without sacrificing brevity.
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 simple update operation and presence of output schema, the description covers basic purpose but lacks context like prerequisites or field dependencies.
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 coverage is 100% with good descriptions for each parameter; the tool description adds no additional meaning, warranting baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Update a project label' with the endpoint, distinguishing it from sibling tools like create, delete, and list.
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 explicit guidance on when to use this tool versus alternatives; the description only restates the name without context for 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 must disclose behavioral traits. It only mentions the API endpoint but fails to describe important aspects like authentication requirements, side effects of creation, error conditions (e.g., missing project_id behavior), or any rate limits. This is insufficient for safe agent invocation.
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 sentence with no wasted words. It is appropriately sized for a clear purpose, though it could be slightly expanded with usage guidance without losing conciseness. Rating 4 for efficient but not perfect communication.
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?
Despite having an output schema (implied), the description does not mention return values, error handling, or any special behavior. Given the complexity of 11 parameters and many options, the description lacks the context needed for a complete understanding. It falls short of what is minimally required.
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 for its 11 parameters, so the description adds no additional meaning. Baseline is 3 because the schema carries the burden; the description's minimal statement does not enhance parameter understanding beyond what is already structured.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create an issue') and the resource ('GitLab REST API /projects/:id/issues'), making the tool's primary purpose unmistakable. It distinguishes from sibling tools like gitlab_get_issue or gitlab_create_merge_request through the specific verb and resource.
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 does not provide explicit guidance on when to use this tool versus alternatives (e.g., gitlab_create_merge_request) or when not to use it. Usage is implied through the purpose, but no contextual advice is given.
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 should disclose behavioral traits. It only says 'Get one issue' without mentioning idempotency, read-only nature, or any side effects. The output schema exists but the description adds no behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no unnecessary words. It is appropriately sized and front-loaded.
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 output schema exists, the description does not need to explain return values. However, it omits details like the possibility of a missing-parameter error or the runtime config behavior for project_id, which are only in the schema. It is adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already describes both parameters well. The description repeats 'by project_id + issue_iid' but adds no new meaning beyond the schema, earning a baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'issue' with the required identifiers (project_id and issue_iid). This distinguishes it from siblings like gitlab_create_issue or gitlab_get_issue_notes.
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 does not provide any guidance on when to use this tool versus alternatives, such as when to use gitlab_get_issue_notes instead. No explicit context or exclusions are given.
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 provided. The description only states 'Get' indicating a read operation, but lacks details on authentication, rate limits, pagination, or potential side effects. Minimal behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with the core purpose. No redundant words, efficient.
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?
Output schema exists, covering return values. However, the description omits behavioral context such as default sorting, the optionality of project_id (runtime config fallback), and error conditions. Adequate but not fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents all parameters. The description reiterates the two key parameters (project_id, issue_iid) but adds no additional meaning beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves issue notes/comments using project_id and issue_iid. It distinguishes from siblings like gitlab_get_issue (issue details) and gitlab_add_issue_comment (add comment).
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. It does not mention when to prefer get_issue_notes over get_issue or add_issue_comment.
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 must disclose behavioral traits. It fails to mention the tool is read-only, the effect of invalid parameters, or any rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no wasted words. It is appropriately sized and front-loaded.
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?
The tool is simple with 2 parameters and has an output schema, so the description need not explain return values. However, it lacks context like required permissions or typical use cases, leaving the agent underinformed.
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 baseline is 3. The description does not add information about parameters beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves merge request changes/diff metadata using the verb 'get' and specific resource. It distinguishes from sibling tools like gitlab_get_merge_request (which gets MR details) and gitlab_get_mr_notes.
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_get_merge_request. No exclusions or prerequisites mentioned.
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 provided, and the description only gives the endpoint. It does not disclose authentication needs, side effects, rate limits, or any constraints beyond what the schema already offers.
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?
Single sentence with no wasted words. Action and resource are front-loaded.
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?
Despite a complete schema and output schema, the description lacks context on tool behavior, such as success outcomes or edge cases. It is minimal for a tool with five parameters.
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. The description adds no further semantic value beyond the endpoint context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action (create) and resource (project label), and provides the API endpoint for reference. Distinct from siblings like gitlab_delete_label and gitlab_update_label.
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?
Implied usage: when a new label is needed. However, no explicit guidance on when to use this tool versus alternatives like gitlab_update_label or gitlab_list_labels.
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, and the description does not disclose any behavioral traits such as authentication requirements, rate limits, or whether all fields are returned. The minimal description fails to add value beyond the verb.
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 concise sentence that front-loads the verb and resource. It earns its place but could be slightly expanded for clarity without becoming wordy.
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 output schema exists and parameters are well-documented, the description is minimally adequate. However, it omits error conditions or any hints about the response structure, which would be helpful for a complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters. The description includes the API path which indirectly shows parameter roles, but it adds no additional semantic meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get merge request detail' with the exact API path, differentiating it from sibling tools like 'gitlab_get_mr_changes' and 'gitlab_get_mr_notes' which handle related but distinct resources.
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?
No explicit guidance on when to use this tool versus alternatives like 'gitlab_get_mr_changes' or 'gitlab_get_mr_notes'. Usage is implied by the resource name, but no exclusions or conditions are provided.
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. The description only states the action without disclosing side effects, error states (e.g., if no approval exists), or permission requirements. Minimal 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, concise sentence that gets straight to the point. No unnecessary words, though it could benefit from slightly more detail.
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 simple nature of the action and the presence of output schema, the description is adequate but lacks details on preconditions (e.g., must be approved first) and possible outcomes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with detailed parameter descriptions. The tool description adds no additional meaning beyond the schema, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Remove approval from merge request' uses a specific verb and resource, clearly indicating the action. It distinguishes from sibling 'gitlab_approve_mr' which adds approval.
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?
No explicit guidance on when to use this tool vs alternatives. The sibling 'gitlab_approve_mr' is present, but conditions like needing prior approval or error states are not mentioned.
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 must carry the full behavioral disclosure. It only states the action and a negative constraint, but lacks details on side effects, error conditions, permissions, or output behavior. With 12 parameters, more context is needed.
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 very brief (two sentences) and front-loads the purpose. No extraneous content. However, given the tool's complexity (12 parameters), it could potentially include more useful information without being wasteful.
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?
Despite the existence of an output schema, the description does not mention return values or behavior details. With 12 parameters, no annotations, and only two sentences of description, the tool is not fully specified for an agent to use reliably.
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 coverage is 100%, so the schema already describes all parameters. The tool description adds no additional parameter information beyond the schema. Some parameters have helpful notes in the schema itself, but the description does not contribute to their semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('append') and the resource ('local issue log markdown'), and distinguishes from siblings by explicitly noting it does not create MR or commit. This is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Use when only local issue log markdown should be appended,' which indicates the primary use case. It also says what the tool does not do (create MR or commit), implying alternative tools. However, it does not explicitly mention sibling tools or when to use 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?
No annotations exist, so the description must fully disclose behavior. It states a write operation (create) but omits side effects, authorization requirements, rate limits, or error scenarios. The auto-generation logic is mentioned but not detailed. Minimal added transparency beyond the tool name.
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?
Two sentences totaling 18 words, front-loaded with the core purpose. Every word earns its place; no fluff.
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 11 parameters and an existing output schema, the description covers the two primary use cases. However, it lacks guidance on when to use this vs siblings, does not mention required permissions or prerequisites, and does not describe the return value (though output schema likely covers it). Adequate but with gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed descriptions. The tool description adds value by summarizing the two usage patterns (direct body vs auto-generation), helping agents understand parameter grouping, but does not add new details beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates an issue note/comment and specifies two modes: direct body or auto-generation from MR changes. The verb 'Create' and resource 'issue note/comment' are precise and distinguish it from sibling tools like gitlab_create_mr_note, which comments on MRs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use direct body vs auto-generation and provides context on how to specify the MR (by IID or source branch). However, it does not explicitly state when not to use this tool or mention alternatives like gitlab_create_mr_note for MR comments. Still, given the sibling list, the differentiation is reasonably clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains that include_base64 triggers downloading images, returning base64, and attaching MCP content blocks. However, it does not disclose potential side effects like network timeouts or permission requirements, which would push it to a 5.
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 consists of two concise sentences. It front-loads the core purpose and then adds a conditional behavior. No redundant words or tangential details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters, an output schema, and reasonable complexity, the description covers the main functionality. However, it lacks details like default values for max_images or the format of image references, which would make it more complete. The presence of an output schema partially compensates.
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 coverage is 100%, so the baseline is 3. The description adds context for include_base64 but does not elaborate on other parameters beyond what the schema says. The schema descriptions are already quite detailed (e.g., project_id usage rules), so the description adds minimal extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool extracts image references from issue description/notes, with a specific verb-noun combination. It distinguishes from sibling tools like gitlab_get_issue, which retrieves issue fields, and gitlab_get_issue_notes, which gets notes. The mention of include_base64 behavior further clarifies the 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 given on when to use this tool versus alternatives. For example, it does not indicate that this tool is for extracting images versus getting the full issue or notes. A user would have to infer usage from the description and sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses core behaviors: verification of clean repo, base branch refresh, preparation_key return, and optional branch creation. Missing details include error handling (e.g., what if repo is not clean) and auth/permissions. The output schema likely covers return structure, but description doesn't reference it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each serving a distinct purpose: set context, state main function and key output, add conditional behavior. No redundant or extraneous information. Efficiency and clarity are excellent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (references preparation_key) and sibling tools, the description covers essential aspects. It could mention error scenarios or prerequisites (e.g., git installed), but overall it provides enough context for an agent to understand when and how to invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed parameter descriptions (e.g., omitting fields unless user provided). The description adds minimal value by contextualizing repo_path as explicit and delivery_method behavior, but it largely summarizes what's already in the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: preparation before delivery workflows. It specifies actions (verify clean repo, refresh base branch, return preparation_key) and distinguishes itself from sibling tools by naming the exact tools that require its output (workflow_issue_to_delivery/workflow_requirement_to_delivery). The verb 'prepare' and resource 'delivery workspace' are specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use before delivery workflows' and names the sibling tools that require the preparation_key, providing clear context for when to use. It also notes the conditional behavior for local_git. However, it does not explicitly state when not to use or mention alternatives beyond the 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?
No annotations provided; description lacks behavioral details like destructive actions, authentication, or failure modes. Only usage guidance is given.
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?
Extremely concise (3 sentences) with clear, actionable information; no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With output schema present and detailed input schema, the description provides sufficient usage context; however, it omits behavioral details.
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 coverage is 100% so baseline 3; description adds no extra parameter meaning beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a GitLab issue from requirement text and stops, distinguishing it from sibling workflow_requirement_to_delivery.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides when-to-use, when-not-to-combine, and what to do if issue already exists (use workflow_issue_to_delivery).
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 must convey behavioral traits. It states 'Local git operations only' and implies fetch/pull and branch switching, but lacks details on side effects like conflicts, uncommitted changes, or branch creation fallback.
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 clearly communicates purpose and scope without unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return values are covered. The description adequately explains the main action and scope, but could mention prerequisites like repository existence or error scenarios for a more complete picture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds significant meaning for 'remote_name' and 'base_branch' by advising to omit unless user-provided and explaining runtime config fallback, which is valuable beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool synchronizes a local repository by fetching/pulling and switching to a target branch. The phrase 'Local git operations only' distinguishes it from sibling tools that operate on remote GitLab resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use when local repository should fetch/pull and switch to a target branch.' It provides clear context for when to use the tool and implies exclusion for remote operations via 'Local git operations only.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It describes the high-level workflow steps but lacks explicit mention of potential side effects (e.g., creating issues, branches, commits, MRs, modifying repository) or required permissions. It does not mention error conditions or what happens on failure.
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 concise (three sentences) and front-loaded with purpose. It efficiently conveys when to use, prerequisite, and exclusions without extraneous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (32 parameters, nested objects, 4 required), the description provides a clear high-level workflow. An output schema exists, so return value details are likely covered there. However, the description could mention expected outcomes (e.g., created MR URL or issue ID) to be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (all parameters have descriptions). The description adds minimal semantic value beyond the schema; it reinforces the prerequisite role of preparation_key but does not enhance understanding of other parameters. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to run the full chain from requirement to delivery (create issue, branch, commit, MR, comment, sync, log). It distinguishes from sibling tools like workflow_requirement_to_issue and workflow_issue_to_delivery by specifying the complete workflow.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool (full chain), what prerequisite to fulfill first (call workflow_prepare_delivery_workspace), and which tools not to combine with. This provides clear guidance for agent selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the two-step behavior, returns of step 1 (review_prompt + diffs), and the posting of comments with optional approval. It lacks details on idempotency, error handling (e.g., both step parameters provided), auth requirements, or rate limits, leaving some behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-structured sentences that front-load the critical two-step workflow information. No redundant or verbose content. Every sentence serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (11 parameters, two-step workflow), the description covers the core workflow and key behaviors. An output schema exists (not shown) but the description explains step 1's return. Lacks details on error conditions (e.g., invalid parameter combinations) and edge cases, but overall sufficient for an AI agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining the workflow roles of key parameters (prepare_review_context and review_comment_body as steps) and the approval parameter, which goes beyond the schema's individual field descriptions. This clarifies how parameters interact in the two-step process.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly defines the tool as a two-step MR review workflow, with specific verbs ('fetch review_prompt + diffs' and 'post the final review comment') and distinct resources. It differentiates from siblings like gitlab_approve_mr and gitlab_create_mr_note by combining review generation and comment posting in a workflow.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly outlines the two-step process (Step 1 with prepare_review_context, Step 2 with review_comment_body) and mentions optional approval. However, it does not explicitly state when to avoid this tool (e.g., for simple approval, use gitlab_approve_mr), though this is implied by sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It accurately describes a read-only operation with no side effects. While it doesn't mention authentication or error scenarios, the tool's simplicity and the presence of an output schema mitigate the need for extensive behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that is front-loaded with the verb and resource. No extraneous information is present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, output schema present), the description is complete. It tells the agent exactly what the tool does and references the API endpoint.
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?
There are no parameters, and schema description coverage is 100%. The description adds no parameter information, but none is needed. Baseline 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('current authenticated GitLab user'), and includes the API endpoint for clarity. It is easily distinguishable from sibling tools which focus on issues, merge requests, etc.
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 when needing the current user's information, but does not provide explicit guidance on when to use this tool versus alternatives. No when-not-to-use or context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses the tool's behavioral scope: it creates a branch, commits, MR, issue comment, performs local sync, and updates the issue log. It warns about the preparation_key rejection if base branch is outdated. No contradictions; the tool's actions are clearly described.
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 paragraph that front-loads the primary purpose. Every sentence adds value: prerequisites, image inspection, idempotency warning, and parameter usage. No redundant or missing information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (26 parameters, 4 required) and the presence of an output schema, the description covers the full workflow: prerequisite call, image review, parameter usage rules, and warnings. The agent has all necessary context to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds significant context beyond the schema. For example, it explains when to omit fields (if not explicitly provided by user), that runtime config overrides apply, and for 'issue_images_reviewed' it specifies the prerequisite action. The commit_actions parameter description ties it to delivery_method.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: delivering an issue end-to-end (branch, commit, MR, issue comment, local sync, issue log). It uses a specific verb 'delivered' and resource 'issue_iid', clearly distinguishing it from sibling tools like gitlab_create_merge_request or workflow_requirement_to_delivery.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use the tool (for existing issue_iid), prerequisites (call workflow_prepare_delivery_workspace first), when to inspect images (if issue may contain image references), and prohibitions ('Do not call this twice for the same issue or same user request'). It also advises checking GitLab first if issue/MR may already exist.
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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