MCP GitLab Server

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 describes the return format ('Concise summary for LLM context') and optimization goals ('Limited context windows', 'Quick understanding'), but doesn't mention important behavioral aspects like whether this makes API calls, potential rate limits, authentication requirements, or error conditions. It provides some context but leaves significant gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely well-structured with clear sections (Returns, Use when, Includes, Optimized for, Related tools). Every sentence earns its place by providing distinct value. The information is front-loaded with the core purpose first, followed by supporting details. No wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only summary tool with 3 parameters and 100% schema coverage, the description provides good contextual completeness. It explains the tool's purpose, when to use it, what it includes, optimization goals, and related alternatives. The main gap is the lack of output schema, but the description does specify 'Returns: Concise summary for LLM context' which provides some output guidance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the schema already thoroughly documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline score of 3 is appropriate since the schema does the heavy lifting, though the description could have provided additional context about parameter interactions or usage patterns.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generate AI-friendly MR summary' with specific verb ('Generate') and resource ('MR summary'). It distinguishes from siblings by explicitly contrasting with 'gitlab_get_merge_request: Full details' and 'gitlab_summarize_issue: Issue summaries', making it clear this is a specialized summary tool for merge requests.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance with 'Use when: Reviewing MRs with AI assistance' and 'Related tools' section that names alternatives. It clearly indicates when to use this tool (for AI-assisted MR review) versus when to use other tools (for full details or issue summaries), providing excellent context for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.