get_thesis_match

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

Annotations indicate read-only and open-world hints, but the description adds valuable behavioral context: it specifies the output includes 5 VCs with fit scores, explanations of interest, and approach guidance. This goes beyond annotations by detailing what the tool returns and its practical benefits, though it lacks explicit rate limits or data source disclosures.

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 functionality in the first sentence, followed by a benefit statement. Every sentence earns its place by conveying essential information without redundancy, making it efficient and well-structured for quick understanding.

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 (matching VCs with scoring and explanations) and lack of output schema, the description provides a good overview of what to expect. However, it could be more complete by specifying output format details or error handling, though annotations cover safety aspects adequately.

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 100% schema description coverage, the input schema already documents all parameters well. The description does not add meaning beyond the schema, as it doesn't explain how parameters like 'productDescription' or 'arrRange' influence the matching process. Baseline score of 3 is appropriate since the schema handles parameter documentation adequately.

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 ('Finds', 'scores', 'explains', 'tells') and resources ('5 VCs', 'investment thesis', 'your company'). It distinguishes itself from siblings by focusing on VC matching with fit scoring and approach guidance, unlike tools like 'get_icp_fit_score' or 'discover_prospects' which have broader or different scopes.

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 clear context for usage ('Saves weeks of investor research'), implying it's for startup fundraising preparation. However, it does not explicitly state when not to use this tool or name alternatives among siblings, such as 'get_syndication_status' for post-investment tracking or 'classify_opportunity' for general opportunity assessment.

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