OpenCollab MCP

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

Annotations already cover key behavioral traits (read-only, non-destructive, idempotent, open-world), but the description adds valuable context by specifying what data is extracted (skills, languages, patterns) and the structured nature of the output. It doesn't contradict annotations and enhances understanding of the tool's scope.

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 front-loaded with the core purpose in the first sentence, followed by specifics on returns. Both sentences earn their place by adding clarity without redundancy, making it efficient and well-structured.

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 the tool's moderate complexity (analyzing profiles), rich annotations, and presence of an output schema, the description is largely complete. It outlines what the tool does and returns, though it could benefit from more usage guidance. The output schema handles return values, so no need to detail them here.

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 0% schema description coverage, the schema only indicates a 'username' parameter without details. The description compensates by clarifying it's a 'GitHub username' and implies the analysis is user-centric, though it doesn't specify format constraints like the 39-character max length from the schema.

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 with specific verbs ('analyze', 'extract') and resources ('GitHub user's profile'), listing concrete outputs like skills, languages, and contribution patterns. It distinguishes itself from siblings by focusing on comprehensive profile analysis rather than specific tasks like checking issues or comparing repos.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, exclusions, or compare it to sibling tools like 'opencollab_match_me' or 'opencollab_contributor_leaderboard', which might overlap in analyzing user data.

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