Gitlab MCP Server
Server Quality Checklist
Latest release: v2.6.6
- Disambiguation5/5
The two tools are clearly distinct: one searches the action catalog, the other executes a specific action. No overlap exists between them, and their descriptions clarify their respective purposes without ambiguity.
Naming Consistency5/5Both tool names follow the same pattern: 'gitlab_' prefix followed by a verb and '_action' (execute_action, find_action). This consistent verb_noun structure makes the tool set predictable and easy to navigate.
Tool Count3/5With only two tools, the server feels thin. However, the two tools are designed as a minimal interface to a larger action catalog, so the count is defensible, but it falls into the borderline territory where more tools might be expected for broader coverage.
Completeness4/5The tool surface covers the core workflow of discovering actions and executing them. A minor gap is the lack of an explicit 'list all actions' tool, but find_action likely covers that through search. Overall, the domain is well-served by these two tools.
Average 4.6/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 2 of 3 community issues answered or closed in the last 6 months
- 126 commits in the last 12 weeks
- Last stable release on
- 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.
This repository includes a glama.json configuration file.
This server has been verified by its author.
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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already flag destructiveHint=true and readOnlyHint=false. The description adds the crucial behavioral constraint that destructive actions require top-level confirm=true, and the structural requirement to pass params as an object. This adds context beyond annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose, then usage requirements, and ends with the alternative recommendation. Every sentence earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the strong annotations, complete schema coverage, and presence of an output schema, the description covers purpose, usage, confirm requirements, and alternatives. It is sufficiently complete for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and the schema already explains all parameters, including the special note about confirm placement. The description repeats some of this but adds no new semantic detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Execute one GitLab catalog action by canonical ID or alias.' It uses a specific verb ('execute') and resource ('GitLab catalog action'), and distinguishes from the sibling tool by noting 'Use find first only when action or params are unclear.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit when-to-use guidance: 'Use find first only when action or params are unclear' provides an alternative. It also provides usage requirements: 'Always pass params as an object' and 'destructive actions require top-level confirm=true.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds value beyond annotations by specifying 'read-only and no GitLab API call' and detailing the return content (schemas, hints, destructive flags, execute examples). Annotations already indicate readOnlyHint and idempotentHint, but the description provides operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently communicates purpose, usage context, and output. No redundant or extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown but indicated), the description does not need to explain return values. It covers the core functionality, inputs, and output contents adequately for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers all three parameters with descriptions, so schema description coverage is 100%. The tool description does not add significant parameter-specific information beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches a local GitLab action catalog, distinguishing it from the sibling 'gitlab_execute_action' which likely executes actions. It specifies the input (query) and output (schemas, hints, flags, examples).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use when the action ID or params are unclear', providing a clear use case. It also states it's read-only and makes no API call, guiding the agent away from using this tool for execution.
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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- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
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