match_company_intel

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

The description discloses key behavioral traits: it only asserts facts derivable from the postings and marks everything else as 'to_research' with a promise to never fabricate. This is critical for an agent to understand the tool's limitations. Since no annotations are provided, the description carries the full burden and handles it well.

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 two sentences, each serving a clear purpose: the first states the overall function, the second specifies behavioral constraints. No redundant or unnecessary information, making it efficient and easy to parse.

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 complexity (deriving intelligence and producing a checklist), the description covers the main aspects: what is asserted, what goes to checklist, and the honesty policy. The existence of an output schema means return values need not be explained. Slight gap: no mention of required input quality or size, but overall sufficient.

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 already describes the parameters (company and jobs). The description adds context that the tool works from job postings, but does not elaborate on parameter format or constraints beyond the schema. Baseline 3 is appropriate as the description adds marginal value.

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: 'Derive company intelligence from job postings + produce research checklist.' It specifies exactly what facts are asserted (open_roles_count, locations, remote_ratio, tech_tags) and what goes into the research checklist (Glassdoor rating, funding, etc.). This distinguishes it from sibling tools like match_score or match_dedupe which have different objectives.

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 implies usage when job postings are available and company intelligence is needed, but it does not explicitly state when to use this tool versus alternatives (e.g., match_keyword_gap, match_score). No explicit when-not-to-use or alternative guidance is provided, making it less helpful for an agent deciding between tools.

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