GitLab MCP Server

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but some overlap exists: get_merge_request_reviews and get_merge_request_details both provide MR information, and get_merge_request_test_report and get_pipeline_test_summary both handle test data, though descriptions clarify their differences (full report vs. summary).
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case, such as get_merge_request_details and create_review_comment. This predictability makes it easy for agents to understand and use the toolset.
Tool Count5/5
With 12 tools, the server is well-scoped for GitLab merge request and pipeline management. Each tool serves a specific function, covering review discussions, MR details, pipelines, and test reports without being overwhelming.
Completeness4/5
The toolset covers key aspects of merge requests, reviews, pipelines, and tests, but lacks broader GitLab operations like repository management or issue tracking. Within its focused domain, it provides good lifecycle coverage with minor gaps.
Average 3.4/5 across 12 of 12 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on April 2, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

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

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

How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the action without behavioral details. It doesn't disclose whether this requires specific permissions, if it's idempotent, what happens on failure, or any rate limits, which is 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is insufficient. It lacks details on permissions, error handling, return values, and differentiation from siblings, leaving significant gaps in understanding how to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters. The description adds no additional meaning beyond implying the 'body' is for discussion content, which is already clear from the schema. This meets the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create a new discussion thread') and resource ('in a merge request review'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'reply_to_review_comment' or 'resolve_review_discussion', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over 'reply_to_review_comment' for responding to existing discussions or 'get_merge_request_reviews' for viewing reviews, leaving the agent 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 carries full burden. It states a read operation ('Get') but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, or response format. For a tool with no annotation coverage, this leaves significant gaps in understanding 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary details. Every word contributes to understanding the tool's function, making it appropriately sized and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a read operation with one parameter, the description is incomplete. It lacks details on return values, error handling, or operational constraints, which are crucial for an agent to use the tool effectively in context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'merge_request_iid' fully documented in the schema. The description adds no additional meaning beyond implying the parameter's role in scoping discussions, matching the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'discussions and comments on commits', specifying the scope 'within a specific merge request'. It distinguishes from siblings like 'get_merge_request_details' or 'get_merge_request_reviews' by focusing on commit-level discussions, but doesn't explicitly differentiate them.
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 like 'get_merge_request_reviews' or 'create_review_comment'. The description implies usage for commit discussions within merge requests, but lacks context on prerequisites, exclusions, or comparative use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'gets' information, implying a read-only operation, but doesn't clarify if it requires authentication, has rate limits, returns paginated data, or what format the output takes. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action ('Get detailed information'), making it easy to parse. Every part of the sentence earns its place by conveying essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a read operation with no output schema) and lack of annotations, the description is incomplete. It doesn't explain what 'detailed information' entails, potential errors (e.g., invalid ID), or behavioral traits like authentication needs. For a tool with no structured output or annotations, more context is needed to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'merge_request_iid' clearly documented as 'Internal ID of the merge request'. The description adds no additional meaning beyond this, as it doesn't explain the parameter's role or constraints. According to the rules, with high schema coverage, the baseline is 3 even without param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('detailed information about a specific merge request'), making the purpose unambiguous. It distinguishes from siblings like 'list_merge_requests' (which lists multiple) and 'get_merge_request_reviews' (which focuses on reviews). However, it doesn't specify what 'detailed information' includes, leaving some ambiguity compared to a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a merge request ID), exclusions, or comparisons to siblings like 'get_merge_request_reviews' or 'list_merge_requests'. This lack of context leaves the agent to infer usage based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Get') but does not describe traits like read-only nature, potential rate limits, authentication needs, response format, or pagination. This leaves significant gaps for an agent to understand how the tool behaves beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no wasted words, front-loading the core action and resource. It efficiently communicates the essential purpose without unnecessary elaboration, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that retrieves data. It does not explain what 'reviews and discussions' entail, the return format, or any behavioral constraints. For a read operation with no structured output documentation, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'merge_request_iid' clearly documented as the 'Internal ID of the merge request'. The description adds no additional semantic context beyond this, such as examples or usage notes, so it meets the baseline of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('reviews and discussions for a specific merge request'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like 'get_merge_request_details' or 'get_commit_discussions', which might also retrieve related information, preventing a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as 'get_merge_request_details' for general info or 'get_commit_discussions' for commit-specific discussions. It lacks any context about prerequisites, exclusions, or recommended scenarios, relying solely on the implied need for merge request reviews.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'List' implies a read-only operation, the description doesn't mention pagination behavior, rate limits, authentication requirements, or what the return format looks like (e.g., list of MR objects with basic fields). For a listing tool with no annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a straightforward listing tool and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool with 3 parameters. It doesn't explain what the tool returns (e.g., array of MR objects with IDs/titles), pagination behavior, or error conditions. For a listing tool that likely returns structured data, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters with descriptions, defaults, and constraints. The description adds no additional parameter information beyond what's in the schema, maintaining the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('merge requests for the GitLab project'), making the purpose immediately understandable. However, it doesn't distinguish this tool from sibling tools like 'get_branch_merge_requests' or 'get_merge_request_details', which also deal with merge requests in different ways.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_branch_merge_requests' (which likely filters by branch) and 'get_merge_request_details' (which likely gets details for a specific MR), there's no indication of when this general listing tool is preferred over those more specific options.
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. While 'Reply to' implies a write operation, it doesn't specify permissions required, whether replies are editable/deletable, rate limits, or what happens on success/failure. This leaves significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every word earning its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is insufficient. It doesn't address behavioral aspects like permissions, side effects, or response format, leaving the agent with incomplete understanding of how to properly invoke and interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds no additional parameter information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Reply to') and target ('a specific discussion thread in a merge request review'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'create_review_comment' or 'resolve_review_discussion', which would be needed for a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'create_review_comment' or 'resolve_review_discussion'. It states what the tool does but offers no context about appropriate use cases, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get all merge requests' implies a read-only operation, it doesn't specify whether this requires authentication, what format the results are in (e.g., list, paginated), or any rate limits or constraints. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core functionality without unnecessary words. It's front-loaded with the main action and resource, making it easy to parse quickly. Every word earns its place, with no redundancy or 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 the tool's simplicity (one parameter, no annotations, no output schema), the description is minimally adequate. It states what the tool does but lacks context on usage, behavior, or output format. For a read operation with no output schema, more detail on return values would be helpful, but the description doesn't provide it, leaving room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'branch_name' clearly documented in the schema as 'Name of the branch'. The description adds no additional semantic context beyond what the schema provides, such as examples or constraints on branch naming. This meets the baseline for high schema coverage but doesn't enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get all merge requests') and the target resource ('for a specific branch'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_merge_requests' or 'get_merge_request_details', which could cause confusion about when to use this specific tool versus alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'list_merge_requests' or 'get_merge_request_details'. It states what the tool does but offers no context about appropriate use cases, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the action without behavioral details. It doesn't disclose required permissions, whether resolution is reversible, effects on notifications, or error conditions (e.g., invalid IDs). For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('resolve or unresolve a discussion thread') with essential context ('in a merge request review'). There is zero wasted verbiage, and every word earns its place by specifying the action, target, and scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks information on permissions, side effects, error handling, and return values. While concise, it doesn't compensate for the missing structured data, leaving the agent with insufficient context for safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are fully documented in the schema. The description adds no additional semantic context beyond implying the tool operates on discussions within merge requests, which is already clear from parameter names and schema descriptions. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('resolve or unresolve'), target resource ('discussion thread'), and context ('in a merge request review'). It distinguishes from siblings like 'create_review_comment' (adds new comments) and 'get_merge_request_reviews' (reads reviews) by focusing on resolution state changes.
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 to toggle discussion resolution status, but provides no explicit guidance on when to use this vs. alternatives like 'reply_to_review_comment' for ongoing discussions or prerequisites like required permissions. It mentions the context ('merge request review') but lacks when-not scenarios or clear alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool's purpose (retrieving logs for debugging) which implies it's a read-only operation, but doesn't explicitly state whether it requires authentication, has rate limits, or what format the output takes. The description adds some context but leaves important behavioral aspects unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with just two sentences that each earn their place. The first sentence states the core functionality, and the second provides valuable context about when to use it. There's no wasted language or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read operation with no output schema, the description provides adequate but minimal context. It explains what the tool does and when to use it, but doesn't address potential limitations, error conditions, or output format details that would be helpful for an agent to properly invoke and interpret results from this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'job_id' well-documented in the schema itself. The description doesn't add any additional parameter information beyond what's already in the schema, so it meets the baseline expectation but doesn't provide extra value regarding parameter usage or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get the trace/log output') and resource ('for a specific pipeline job'), distinguishing it from siblings like get_merge_request_test_report or get_pipeline_test_summary. It provides a concrete use case ('debugging failed tests and understanding CI/CD failures') that makes the purpose immediately understandable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('Perfect for debugging failed tests and understanding CI/CD failures'), which helps the agent understand the appropriate scenarios. However, it doesn't explicitly mention when NOT to use it or name specific alternatives among the sibling tools, which would be needed for a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool returns pipeline data with job statuses and IDs, which is useful behavioral context. However, it doesn't mention potential limitations like error handling, rate limits, authentication needs, or whether it's a read-only operation, leaving gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the purpose and scope, the second explains the utility of returned data. Every sentence adds value without redundancy, making it 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 no annotations and no output schema, the description partially compensates by explaining the return data includes jobs and statuses with IDs usable for get_job_log. However, for a tool that fetches pipeline data, it lacks details on response format, error cases, or data freshness, leaving room for improvement in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents the merge_request_iid parameter. The description doesn't add any parameter-specific details beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get the last pipeline data'), target resource ('for a specific merge request'), and scope ('including all jobs and their statuses'). It distinguishes from siblings like get_merge_request_details or get_pipeline_test_summary by focusing on pipeline data rather than general MR info or test summaries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by specifying it retrieves 'the last pipeline data' and mentions job IDs can be used with get_job_log, implicitly suggesting when to use this tool versus get_job_log. However, it doesn't explicitly state when not to use it or compare with alternatives like get_merge_request_test_report or get_pipeline_test_summary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool retrieves test data (implying read-only behavior) and specifies the content includes failures, errors, and stack traces. However, it lacks details on permissions, rate limits, or response format, which are important for a tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by specific details and usage guidance in just two sentences. Every sentence adds value: the first defines the tool's function and scope, and the second provides clear usage context without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations and no output schema, the description is moderately complete. It covers purpose and usage well but lacks details on behavioral aspects like authentication or response structure, which are important for debugging tools. It compensates somewhat with specific content details but leaves gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single parameter (merge_request_iid) with its type and description. The description does not add any additional meaning or context about the parameter beyond what the schema provides, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get structured test report') and resource ('for a merge request'), distinguishing it from siblings like get_merge_request_details or get_pipeline_test_summary by focusing on test failures, error messages, and stack traces. It explicitly mentions the data is 'the same test data visible on the GitLab MR page,' which adds specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'Best for debugging test failures.' This clearly indicates its primary use case and distinguishes it from alternatives like get_pipeline_test_summary (which might show summaries) or get_merge_request_details (which covers general info).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it's a read operation ('Get'), provides a 'lightweight overview' with 'pass/fail counts per test suite', and is performance-optimized ('Faster than full test report'). It doesn't mention rate limits or authentication needs, but covers core functionality 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 perfectly concise and front-loaded: three sentences that each earn their place by defining purpose, differentiating from alternatives, and providing usage context with zero wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with one parameter and no output schema, the description is nearly complete: it explains what the tool returns ('pass/fail counts per test suite') and its performance characteristics. It could mention the return format more explicitly, but given the low complexity, it's sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents the single parameter. The description adds no additional parameter semantics beyond what's in the schema, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('get test summary') and resource ('for a merge request'), distinguishing it from siblings like 'get_merge_request_test_report' by emphasizing it's a 'lightweight overview' and 'faster than full test report'.
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?
It explicitly provides usage guidance by stating when to use this tool ('Great for quick status checks') and when to use an alternative ('Faster than full test report'), clearly differentiating it from 'get_merge_request_test_report' without needing to name it directly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![gitlab-mr-mcp MCP server](https://glama.ai/mcp/servers/amirsina-mandegari/gitlab-mr-mcp/badges/card.svg)](https://glama.ai/mcp/servers/amirsina-mandegari/gitlab-mr-mcp)

Score Badge

Copy to your README.md:

[![gitlab-mr-mcp MCP server](https://glama.ai/mcp/servers/amirsina-mandegari/gitlab-mr-mcp/badges/score.svg)](https://glama.ai/mcp/servers/amirsina-mandegari/gitlab-mr-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/amirsina-mandegari/gitlab-mr-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server