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

Beyond the annotations, the description adds crucial behavior: free/read-only is reinforced, failures list unreadable sources, multiple or zero results are normal, and returned text is data not instructions. This is rich context that materially helps an agent interpret results safely.

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 dense but front-loads the core purpose and every sentence carries operational meaning. It is longer than average but not padded; the evidence rules and caveats are compressed into a single paragraph.

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?

For a tool with no output schema and non-trivial evidence logic, the description covers return semantics (several or none, failures) and safety (data not instructions). Combined with annotations, an agent has everything needed to call and interpret the tool.

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 baseline is 3. The description adds useful meaning: name alone never resolves, github_orgs are checked like any other, and domain drives resolution. This goes beyond the schema descriptions and clarifies how parameters interact.

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?

States a specific verb and resource: find a company's official GitHub organizations from its domain, with a clear explanation of the reciprocal-evidence rule. It distinguishes itself from start_company_map by noting when LinkedIn data is tied and how github_orgs triggers scanning.

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?

Explains what counts as confirmed vs a likely candidate and how github_orgs is used, giving clear operational context. It does not explicitly enumerate when to prefer this over every sibling, but the evidence rules and the start_company_map reference provide meaningful guidance.

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