gitlab-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct operation (e.g., get_job vs get_job_log vs poll_job are clearly different). No two tools have overlapping purposes; all are well-separated.
Naming Consistency5/5
All tools follow the consistent pattern `gitlab_verb_noun` in snake_case. Examples: gitlab_create_merge_request, gitlab_get_job_log, gitlab_trigger_pipeline. No deviations.
Tool Count5/5
20 tools cover a broad yet focused scope (projects, MRs, CI/CD, files, branches). Each tool serves a clear purpose without redundancy, making the set well-scoped for a GitLab integration.
Completeness3/5
Key operations are present (create/get/list MRs, CI triggers, job management), but missing write actions like merge/close MRs, create/update files, or manage branches. This leaves notable gaps for full lifecycle coverage.
Average 3.6/5 across 20 of 20 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 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
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}
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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?
With no annotations, the description carries full burden but only states it retries failed jobs. It does not disclose important behaviors such as idempotency, side effects on non-failed jobs, authentication needs, or rate limits. Insufficient 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short (two sentences plus args list) but lacks structure. It conveys purpose efficiently but could be more informative without adding length. Mediocre conciseness; not wasteful but not optimized.
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 (which reduces need to explain returns), the description omits prerequisites like pipeline existence, user permissions, and what happens if the pipeline is already retrying. Incomplete for a reliable agent decision.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description adds minimal context: pipeline_id is numeric, project_path is a GitLab path with example 'group/project'. It does not explain where to find these values or specify format constraints. Some value but far from compensating for the coverage gap.
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 'Retry all failed jobs in a pipeline.' This is a specific verb+resource combination that distinguishes from sibling tools like gitlab_retry_job (single job) and gitlab_trigger_pipeline (new pipeline).
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 that it retries only failed jobs, nor does it suggest conditions like pipeline existence or permissions. No explicit when-not or alternatives 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, and the description does not disclose any behavioral traits such as side effects (e.g., source branch deletion), authorization needs, or error conditions. The description only states what the tool does, not how it behaves.
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 structured as a clear docstring with bullet points, each line providing specific parameter info. It is concise without unnecessary wording, though it could be slightly more compact by integrating parameter names.
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 (7 parameters) and presence of an output schema, the description adequately covers parameter meanings but lacks details on error handling, default behavior effects (e.g., squash, remove_source_branch), or required conditions. It is minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning beyond the input schema by explaining each parameter's purpose (e.g., 'description: Optional MR description / body text'). The schema only has titles and types with no descriptions, so the docstring fills the gap effectively.
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 'Create a new merge request' and lists parameters. However, it does not differentiate from sibling tools like 'gitlab_add_mr_note' or 'gitlab_get_merge_request', but the verb+resource is specific enough.
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 vs alternatives, such as for updating existing MRs or adding notes. No prerequisites or constraints are mentioned (e.g., branches must exist, project must be specified).
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 for disclosing behavioral traits. It does not state that the operation is read-only, safe, or what happens on missing or invalid project paths. No mention of authentication or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences, front-loading the purpose. The arg description is terse but could be integrated into the first sentence for even better efficiency.
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, the description should explain what metadata is returned and that the parameter can be obtained from gitlab_list_projects. It does not address the optional parameter behavior or any permissions.
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 0%, so the description must add meaning. It provides an example format ('group/project') for project_path, adding value beyond the schema's type. However, it does not clarify that the parameter is optional (default null) or the implications of omitting it.
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 metadata for a GitLab project', specifying the verb (get) and resource (metadata for a project). This distinguishes it from sibling tools like gitlab_list_projects (which lists multiple projects) and other get_* tools for different entities.
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 the project path from gitlab_list_projects, 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?
No annotations are provided, so the description carries full burden. It does not disclose behavioral traits such as read-only nature, authorization requirements, rate limits, or side effects. The description only states what the tool does, not how it behaves.
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 concise: two sentences for purpose followed by parameter documentation. It front-loads the main action and avoids unnecessary words.
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?
With an output schema present, the description need not detail return values, but it does mention 'details and diff stats'. It lacks information on error conditions, pagination (if any), or behavior for invalid SHAs. Suitable for a simple get tool but could 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 description coverage is 0%, so the description must add meaning. It explains the 'sha' parameter as 'Full or short commit SHA' and 'project_path' with an example, which adds value beyond the bare schema. However, it does not provide constraints like min/max length or format.
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 details and diff stats for a specific commit', specifying the verb (get), resource (commit), and output (details and diff stats). It distinguishes from siblings like gitlab_list_commits which lists multiple 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?
No guidance is provided on when to use this tool versus alternatives (e.g., gitlab_list_commits for searching). The description does not mention prerequisites, context for use, 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It only states it lists jobs with statuses, but lacks details on pagination, filtering, mutation safety, or any side effects.
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 brief and front-loaded with the core functionality. No redundant information, though the parameter list is necessary and well-placed.
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 an output schema exists, the description adequately covers purpose and parameters but lacks usage guidance and behavioral context, which is a gap for a tool with no 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?
The argument list adds minimal context beyond the schema (e.g., 'numeric' for pipeline_id, example for project_path). Schema coverage is 0%, so description partially compensates but not extensively.
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 (list), resource (jobs), and scope (in a specific pipeline) with added detail about statuses. It distinguishes from siblings like 'gitlab_get_job' by specifying it's for all jobs in a pipeline.
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 vs alternatives, no prerequisites, and no when-not-to-use information. The description is purely functional without comparative 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 exist, and the description does not disclose behavioral traits (e.g., whether comments are appended, trigger notifications, or require permissions). For a mutation tool, more transparency 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 concise with a single-line purpose followed by parameter details. The 'Args' block is slightly verbose but overall efficient 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 description covers parameters adequately and the output schema (present) handles return values. However, it lacks usage context, side effects, and behavioral details, making it just adequate for a simple tool.
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?
With 0% schema description coverage, the description adds meaningful context: mr_iid is an IID like 42, body supports markdown, and project_path is a GitLab path. This compensates for the sparse schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action and resource: 'Add a comment to a merge request.' This is specific and distinguishes it from siblings like gitlab_create_merge_request or gitlab_get_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 is provided on when to use this tool versus alternatives. There is no mention of prerequisites, conditions, 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?
No annotations exist, so description must fully disclose behavior. It says 'cancel' but omits consequences (e.g., job becomes canceled state, pipeline impact, permissions needed, irreversibility). Inadequate 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is short and front-loaded with purpose. The Args section is clear, though not overly structured. No superfluous text.
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 low schema coverage and no annotations, description should provide more context: return values, preconditions (e.g., job must be running), errors (e.g., cannot cancel already finished job). Output schema exists but description doesn't reference it.
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?
With 0% schema coverage, description adds meaning: explains job_id as 'numeric job ID to cancel' and project_path as 'GitLab project path'. This compensates for missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Cancel a running CI job', which is a specific verb+resource. It distinguishes from siblings like gitlab_retry_job (retry) and gitlab_get_job (read).
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?
Description does not provide explicit guidance on when to use vs alternatives, such as when a job is stuck or when cancellation is allowed. It only states the action, leaving usage context inferred.
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 bears full responsibility for behavioral disclosure. It states the read operation but does not mention what form the content is returned in (e.g., raw, base64), whether authentication is required, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with no extraneous text. The Args block is clear, though slightly non-standard for MCP, but still effective.
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 existence of an output schema, the description does not need to detail return values. However, it could mention that the response includes file content and encoding. Overall, it is adequate but not fully complete.
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?
Despite 0% schema description coverage, the description adds meaningful explanations for all three parameters, including examples and default values. This compensates fully for the schema's lack of 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 action: 'Retrieve the contents of a file from the repository.' This is a specific verb and resource, and it is distinguishable from sibling tools like gitlab_get_commit or gitlab_get_project.
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, nor does it mention any prerequisites or exclusions. Sibling tools exist, but the description does not help an agent choose.
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 does not disclose behavioral traits such as read-only nature, rate limits, or error conditions. It only describes parameters but does not address what happens if the job is not finished or if the log is large.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: a one-line purpose followed by a clear argument list. Every sentence adds value with no fluff, and the structure front-loads the purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple fetch tool with an output schema, the description covers purpose and all parameters adequately. It does not mention error handling or preconditions, but these are not critical for basic usage.
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% description coverage, so the description compensates by explaining all three parameters. It provides the format for job_id, an example for project_path, and default/meaning for tail. However, project_path explanation could be more precise.
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 fetches the raw log output of a CI job, providing a specific verb and resource. It distinguishes from sibling tools like gitlab_get_job, which likely retrieves job metadata, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage 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 like gitlab_get_job or gitlab_list_jobs. The description is purely functional and lacks 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 carries the full burden. It explains the parameters but does not disclose behavioral traits such as pagination behavior, rate limits, or ordering. The description lacks depth for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise at 5 lines, with a clear structure: purpose line followed by parameter explanations. Every sentence adds value, and the format is 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?
The description adequately covers the three parameters, but given the absence of annotations and the existence of an output schema, it could benefit from mentioning ordering or filtering context. It is minimally viable but leaves gaps for a simple list tool.
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 description coverage is 0%, but the description adds significant meaning: it explains each parameter (e.g., project_path uses 'group/project' format, ref accepts branch/tag/SHA, limit has default and max). This compensates fully for the missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List commits on a branch or ref,' using a specific verb and resource. It distinguishes itself from siblings like gitlab_get_commit (single commit) and gitlab_list_branches (branches).
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 implies usage for listing commits on a branch but provides no explicit guidance on when to use this tool versus alternatives. There is no mention of when not to use it or any context about ordering or pagination.
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 must fully disclose behavioral traits. It only states the action without detailing side effects (e.g., resource consumption, job execution), authentication needs, or potential errors. This is insufficient for an agent to understand the full implications of triggering a pipeline.
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 appropriately sized with a clear first sentence and a structured list of arguments. It avoids redundancy but could be slightly more succinct. Overall, it is well-organized 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?
The description covers the core parameters but lacks context about the output (though an output schema exists), asynchronous behavior, or any constraints. For a trigger action, more details on the resulting pipeline state would be helpful. It is adequate for basic use but not fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides clear semantic meaning for all three parameters: ref (branch/tag/SHA), project_path (with example), and variables (optional dict). This adds value beyond the bare schema types and names.
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 triggers a new CI pipeline on a specific branch or tag. This distinct verb+resource combination differentiates it from sibling tools like gitlab_get_pipeline (read-only) or gitlab_retry_pipeline (re-triggering an existing pipeline).
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 implicitly conveys when to use the tool (to start a new pipeline), but it provides no explicit guidance on when not to use it or which alternative tool to consider. For instance, it doesn't mention gitlab_retry_pipeline for retries or gitlab_get_pipeline for status checks.
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 bears full responsibility for behavioral disclosure. It only states 'get the current status' without explicitly confirming it is a read-only operation with no side effects or specifying response structure, rate limits, or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded with the purpose. The Args section is efficiently formatted with one sentence per parameter. Could be slightly more structured (e.g., bullet points) but is effective as-is.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and the presence of an output schema, the description adequately covers the core purpose. It does not need to explain return values due to output schema. However, for a job status tool, it could briefly note that the response includes status field, but not required.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description adds essential meaning. It explains that 'job_id' is numeric and visible in the GitLab UI URL, and 'project_path' is in format 'group/project' with a default from environment variable. This is clear and helpful.
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?
Description uses specific verb 'get' and resource 'current status of a GitLab CI job by ID'. Clearly distinguishes from sibling tools like 'gitlab_list_jobs' (which lists multiple jobs) and 'gitlab_poll_job' (which polls for completion).
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?
Description implies usage for retrieving a single job's status but does not explicitly state when to use this tool versus alternatives like 'gitlab_poll_job' or 'gitlab_cancel_job'. No guidance on prerequisites or conditions.
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 states 'Get details' implying a read operation, but does not mention that it is non-destructive, safe, or any rate limits/auth needs. The description is minimal and does not go beyond the basic purpose.
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?
Description is extremely concise: two sentences plus two parameter docstrings. Every sentence adds value without redundancy. Front-loads the purpose and then details parameters efficiently.
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 simplicity of the tool (get by IID) and presence of an output schema, the description is adequate. It identifies the key parameters and their purpose. Could mention that it returns the full merge request object, but the output schema likely covers that. Minor gap: no mention of error cases or required permissions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description carries full burden. It explains 'mr_iid' as the IID shown in UI (e.g., !123) and 'project_path' with example 'group/project', adding meaningful context beyond the schema's empty descriptions. However, it does not specify that 'project_path' is optional or default behavior.
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 'Get details of a merge request by its project-level IID.' The verb 'Get' and resource 'details of a merge request' are specific. The mention of project-level IID distinguishes it from global MR lookups and sibling tools like 'gitlab_list_merge_requests'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage when a specific MR IID is known but does not explicitly mention when to use this over alternatives like 'gitlab_list_merge_requests' for listing. No exclusion criteria or context for when not to use it 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, so the description must disclose behavioral traits. It mentions listing with filters and limits but omits details on pagination, ordering, side effects, and authentication requirements. The default null for project_path is ambiguous.
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 very concise: two sentences plus a structured bullet list for parameters. No redundant information, each sentence serves a clear purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the description need not explain return values. It covers the filtering and limit parameters well, but does not mention that project_path may be required despite being optional, and lacks authentication context. Overall reasonably complete for a list tool.
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 0%, but the description explains all three parameters: project_path (with example), state (with allowed values and default), and limit (with default and max). This adds significant 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 'List merge requests for a project', specifying the verb, resource, and scope. It distinguishes from siblings like gitlab_get_merge_request which retrieves a single MR.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage 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, but the purpose implies it for listing. The description provides parameter details but no when-to-use or when-not-to-use context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It only states that the tool retries a job, which is a mutation, but does not disclose any side effects, required permissions, or potential error states. The behavioral disclosure is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: one line for purpose and two lines for parameter details, with no wasted words. The purpose is stated first.
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 and the presence of an output schema, the description covers the essentials. However, it lacks context about job state prerequisites or error handling, which would enhance completeness for an AI agent.
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 documentation coverage is 0%, so the description must provide parameter meaning. It explains job_id as 'numeric job ID' and project_path with a format example ('group/project'), adding value beyond the bare schema types.
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 ('retry') and the resource ('failed or cancelled CI job'). It distinguishes this tool from siblings like gitlab_cancel_job and gitlab_retry_pipeline by specifying the scope (job-level, not pipeline-level).
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 usage when a CI job has failed or been cancelled. It does not explicitly exclude other states or mention alternatives, but the context is clear enough for an AI agent.
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 bears full responsibility. It mentions 'Get the status and summary' implying a read-only operation, but does not disclose behavioral details such as authentication needs, permission requirements, or any side effects. Basic but not comprehensive.
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: the purpose is stated in the first sentence, followed by two terse parameter explanations. No unnecessary words, well-structured for quick parsing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, 1 required, output schema present), the description covers essential aspects. It could be improved by noting any required permissions or version constraints, but is adequate for basic usage.
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 description explains both parameters with clear definitions and an example for project_path. Schema description coverage is 0%, so the description adds significant value by clarifying parameter meanings beyond the bare 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 verb 'Get' and the resource 'status and summary of a GitLab CI pipeline.' It distinguishes from sibling tools like gitlab_list_pipelines (listing) and gitlab_retry_pipeline (manipulation), making the tool's purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for fetching a single pipeline's details but provides no explicit guidance on when to use this over alternatives like gitlab_list_pipelines or gitlab_get_commit. It lacks context about prerequisites or decision points.
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 provided, so description must carry full burden. It describes parameters and defaults but doesn't mention behavior on empty results, pagination, or auth requirements beyond 'accessible to authenticated user'.
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?
Description is extremely concise: one sentence for purpose, then a bullet list of parameters. No fluff, front-loaded with essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool, the description covers the purpose and parameters adequately. Output schema exists to explain return values, but missing details like ordering or pagination behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must compensate. It explains the 'search' and 'limit' parameters, including default and maximum values, adding meaning beyond the schema's type definitions.
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 'List GitLab projects accessible to the authenticated user.' This is a specific verb and resource, distinguishing it from sibling tools like gitlab_list_branches and gitlab_list_commits.
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, but the purpose is straightforward; implied usage for listing projects.
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?
Annotations are absent, so the description must carry the burden. It mentions default and max limit but does not disclose error handling, authentication needs, or confirm read-only nature. This is adequate but lacks full 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 extremely concise, using a single sentence for purpose and a three-line arg list. No unnecessary words, effectively front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with an output schema, the description covers purpose and all parameters. Missing details like error behavior or project existence checks, but overall sufficient.
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 description coverage is 0%, so the description fully compensates by explaining all three parameters: project_path (with example), search (optional keyword), and limit (with default and max). Adds significant value beyond the schema's titles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List branches in a project,' which is a specific verb-resource pair. There is no sibling tool for listing branches, making it unambiguous.
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. While the purpose is clear, there is no mention of context or exclusions, so usage is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It mentions 'most recent' but does not disclose ordering (e.g., by date descending), pagination (beyond limit/max), or that it is a read-only operation. Partial transparency, but missing details.
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: one line for purpose, then two bullet-like lines for parameters. No unnecessary words, and the most important information (purpose) is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing tool with two parameters, the description covers the basics well. It does not mention the output format, but an output schema exists (not shown). Could add context about ordering or response structure, but overall adequate.
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?
Both parameters are explained with examples and constraints: `project_path` is described as 'GitLab project path, e.g. 'group/project'', and `limit` has default 10 and max 100. This adds significant value beyond the empty schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and resource ('pipelines for a project'), clearly distinguishing it from siblings like `gitlab_get_pipeline` which fetches a single pipeline. It states it lists the 'most recent' pipelines, adding specificity.
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 when-to-use or when-not-to-use guidance is provided. The description implies usage for listing recent pipelines but does not compare with alternatives like `gitlab_get_pipeline` for detailed view or `gitlab_list_jobs` for pipeline jobs. Usage is clear but not differentiated.
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 full burden. It explains polling behavior, interval, timeout, and return value. It does not cover error handling or rate limits, but provides key behavioral traits adequately.
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 (7 lines) and front-loaded with the primary purpose. Every sentence adds value, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers input parameters and return value. It lacks explicit mention of authentication or prerequisites, and does not clarify if project_path is always needed (it's optional in schema). Given the presence of an output schema, the return description is sufficient, but minor contextual gaps remain.
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 0%, so description must add meaning. It explains each parameter (job_id, project_path, interval_seconds, timeout_minutes) with types, defaults, and examples (e.g., 'group/project' for project_path), significantly augmenting the raw 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 verb 'Poll' and the resource 'GitLab CI job', and specifies the outcome 'until it reaches a terminal state'. This distinguishes it from siblings like gitlab_get_job which just fetches status once.
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 explains the polling mechanism with interval and timeout, implying when to use it (when waiting for completion). However, it does not explicitly mention alternatives or when not to use it, so it is clear but not fully explicit.
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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