lookup_github_repo

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

No annotations are provided, so the description carries full burden. It states the tool is read-only ('Get' and 'Returns'), works only with public repos, and describes the nature of the returned signals (behavioral commitments). It does not mention rate limits or authentication, but the read-only nature is clear.

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 well-structured with a first paragraph explaining functionality and a second paragraph for use cases and examples. It is concise without redundant words, but could be slightly tighter.

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 low complexity (one parameter, no nested objects), the description fully covers what the tool does, what it returns, and when to use it. No output schema is present, but the listed signals provide adequate expectations.

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% with the 'repo' parameter's description already explaining the format and providing an example. The tool description repeats the example without adding new meaning beyond the schema, so baseline of 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 it gets a 'behavioral commitment profile' for any public GitHub repository and lists the returned signals. It distinguishes itself from sibling tools which cover different domains (business, npm, Go modules, etc.).

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 lists use cases: 'vetting open-source dependencies, evaluating AI tools/frameworks, assessing vendor reliability.' It provides examples but does not specify when not to use or mention alternatives. Since siblings cover other package types, the context is clear enough.

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