OpenCollab MCP

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

Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context by specifying the exact data sources fetched (issue body, comments, labels, contributing guidelines, directory structure) and the purpose (PR planning), which goes beyond annotations. No contradiction with annotations.

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 two sentences, front-loaded with the main purpose and followed by specific data sources. Every sentence adds value: the first states the goal, the second lists exactly what is fetched. 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?

Given the tool's complexity (gathering multiple data sources), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (which handles return values), the description is complete. It clearly states the purpose, data gathered, and usage context without needing to repeat structured information.

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 description coverage is 0%, but the description does not mention parameters at all. It implies parameters through context (e.g., 'GitHub issue'), but provides no details on required inputs like owner, repo, or issue number. Baseline is 3 since the schema fully documents the single nested parameter object with good descriptions.

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 ('gather', 'fetches') and resources ('GitHub issue', 'issue body, comments, labels, contributing guidelines, and repo directory structure'). It distinguishes itself from sibling tools by focusing on comprehensive context gathering for PR planning rather than analysis, matching, or health checks.

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 implies usage context ('so the AI can draft a PR plan') and specifies what data is gathered, but does not explicitly state when to use this tool versus alternatives like 'opencollab_issue_complexity' or 'opencollab_find_issues'. It provides clear context but lacks explicit exclusions or named alternatives.

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