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

No annotations are provided, so the description carries the full behavioral disclosure burden. It does add useful behavioral context: the tool returns a brief with citations, business profile, competitor analysis, market signals, and risks, and it costs $0.50/call via x402. However, it does not disclose failure modes, payment/authorization issues, data freshness, or any side effects.

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 a single efficient sentence that front-loads the main deliverable and includes the most decision-relevant detail, the cost. It avoids boilerplate and repetition. The 'via x402' phrase is compact but may be unclear to agents unfamiliar with the x402 payment context.

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?

The description lists the key output categories, so an agent can form a reasonable expectation of the result. However, it does not explain how filing_type changes the output, what the response structure looks like, or how errors such as unknown tickers or payment failures are handled. There is no output schema to compensate for those gaps.

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?

The schema documents ticker with an example and filing_type as an enum with a default, but the description adds no parameter-specific meaning. It does not explain how filing_type affects the research brief or whether ticker alone is sufficient. With 50% schema coverage and a self-explanatory enum, the schema carries most of the semantic load.

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 opens with a concrete deliverable ('Company research brief') and lists its contents: cited sources, business profile, competitors, market signals, and risks. It clearly indicates the tool produces company-focused research, which distinguishes it from broader market-intelligence siblings. It lacks an explicit verb like 'retrieves' or 'generates,' but the noun phrase conveys purpose effectively.

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 does not state when to use company_research versus related siblings such as market_intel, answer_with_sources, or web_extract. There are no exclusion criteria, prerequisites, or routing hints. An agent must infer usage solely from the tool name and the output description.

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