CERN GitLab MCP Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Adds valuable scope context ('CERN GitLab', 'discussions') not fully captured in schema parameter descriptions. However, lacks disclosure of pagination behavior, result ordering, result format, or safety profile (read-only nature).

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?

Two well-structured sentences with zero waste. First sentence defines the core action and scope; second provides use case context. Information is front-loaded and appropriately sized for the tool's complexity.

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?

Given 4 simple parameters with complete schema coverage, the description adequately covers tool purpose and usage context. Minor gap: no output schema exists, and description does not specify what data structure or fields are returned in results.

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?

Schema has 100% description coverage, establishing baseline 3. Description provides high-level use cases that implicitly guide search_term content but does not add explicit parameter syntax, format constraints, or examples beyond what the schema already documents.

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?

Specific verb ('Search') + resource ('issues and discussions') + scope ('CERN GitLab projects') clearly stated. Explicitly distinguishes from sibling tools like search_code and search_projects by specifying the exact resource type (issues vs code/projects).

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?

Provides clear positive guidance via three specific use cases (understanding library usage, finding error solutions, checking feature support). However, lacks explicit 'when not to use' guidance or direct comparison to alternatives like search_code.

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