Interpret prose into a proposed research document

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

The description discloses several behavioral traits beyond the annotations: the result is labeled proposal:true, it is never executed here, output includes unsupported[] stand-ins and clarify notices, it requires the research:interpret scope, and each call spends provider money. This goes well beyond the readOnly and openWorld hints, and 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 four sentences, each serving a distinct purpose: purpose, proposal nature, workflow with run_scan, and auth/cost. It is front-loaded with the core function and contains no filler or redundant information.

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 there is no output schema, the description compensates by explaining the output (proposal:true, unsupported[] stand-ins, clarify notices), the workflow (show to user, then run_scan), and important operational details (scope and cost). It covers all essential aspects for an agent to correctly select and invoke this 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%, with both language and time_zone already fully described in the input schema. The description adds general context (e.g., 'plain-language market question') but no parameter-specific details or new meaning beyond the schema, so the baseline 3 is appropriate.

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 function: 'Turn a plain-language market question into a PROPOSED research_query.v2 document.' It emphasizes that the result is a proposal, never executed, which distinguishes it from the sibling run_scan tool that executes scans. The verb 'Turn' and specific output resource make the purpose explicit and unambiguous.

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 gives clear usage context: it says to show the proposal to the user and then call run_scan to execute, framing this tool as the first step in a two-step workflow. It also notes the required scope and metering. However, it does not explicitly state when not to use the tool or provide alternative scenarios beyond run_scan, so it falls short of a 5.

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