Find Similar Companies

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

The description adds valuable behavioral context beyond what annotations provide: it explains the similarity algorithm ('characteristics like industry, stage, business model'), describes the return format in detail with JSON structure, and provides 'Next Steps' guidance. While annotations cover safety (readOnly, non-destructive, idempotent), the description adds practical implementation details. No contradiction with annotations exists.

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 clear sections (What it does, Input, Returns, Next Steps, Example workflow), front-loads the core purpose, and every sentence earns its place by providing distinct value. It's comprehensive without being verbose, using bullet points and JSON examples efficiently.

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 moderate complexity, rich annotations, and 100% schema coverage, the description provides excellent completeness: it explains the algorithm, provides input examples, details the return format (compensating for no output schema), gives next steps, and includes a full workflow example. It covers all necessary aspects for an agent to understand and use this tool effectively.

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 baseline is 3, but the description adds meaningful context: it clarifies company_id accepts 'either numeric ID or full URN' with examples, explains the algorithm behind similarity matching, and provides workflow context that helps understand parameter usage. However, it doesn't add significant value beyond the well-documented schema for size and response_format parameters.

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 verb ('find') and resource ('companies similar to a given company'), and explicitly distinguishes it from siblings by mentioning the core VC use case and referencing harmonic_lookup_company and harmonic_get_company as complementary tools. It goes beyond the title to explain the algorithm and use case.

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 explicit guidance on when to use this tool versus alternatives through the 'Example workflow' section, which shows harmonic_lookup_company should be used first to find a company, then this tool for similarity, then harmonic_get_company for details. It also mentions this is the 'core use case for VCs' and references sibling tools by name.

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