github-repo-intel

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

With no annotations provided, the description discloses that it uses the free GitHub public API (60 req/hr, no key needed). It also explains that include_release incurs an extra API call. While it doesn't detail error handling or rate limit behavior, the API limit mention is valuable transparency.

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 concise (3 sentences) and front-loaded with purpose. Every sentence adds value, though it could be slightly tighter by combining the use case list with the data fields.

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 no output schema, the description thoroughly lists all return fields (stars, forks, etc.). It also provides usage context and API limitations, making it nearly self-sufficient for an AI agent.

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 coverage is 100%, so the schema fully documents both parameters. The description adds minimal extra value (e.g., 'extra API call' for include_release, but that's already in the schema description). Thus, baseline 3 is appropriate.

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 provides GitHub repository intelligence (stars, forks, issues, etc.) on a single repo, distinguishing it from sibling tools like github-org-intel. The verb 'intel' and resource 'GitHub repo' are specific, and the list of data fields is exhaustive.

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 explicitly mentions use cases: before wiring a library, evaluating acquisition, assessing community health. However, it does not explicitly say when not to use it or mention alternative tools for org-level queries, which would elevate it to a 5.

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