research

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

With no annotations provided, the description carries the full behavioral disclosure burden and does so richly. It explains the iterative loop, polling behavior with the exact `{"state": "researching"}` reply, billing semantics ('Billed as ONE research run... ONLY when it completes; polling is free'), grounding constraints ('written ONLY from those passages - never invented'), and that the answer names coverage gaps.

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 long but information-dense, with the core purpose front-loaded and alternative tools explicitly excluded. Each sentence adds meaningful operational or selection detail, though the length could be trimmed slightly without losing critical content; it does not waste words.

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?

This is a complex, multi-phase research tool with no annotations and a high-stakes polling/billing model. The description covers the full workflow, return shape ('cited evidence pack (`passages` with source urls)' and 'grounded `answer`'), polling contract, billing rules, and honesty about unsupported conclusions. The presence of an output schema means return-values don't need full enumeration, but the description goes well beyond minimum.

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 has zero description coverage, so the description must compensate. It provides strong semantics for the query parameter (the kind of question to investigate) and indirectly covers include_answer by mentioning 'by default, a grounded answer.' However, max_tokens is never explained in the description, and the schema itself only provides a title and default, leaving this parameter's purpose ambiguous.

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 strong verb and resource: 'INVESTIGATE an open question that has no ready-made answer sitting on any single page.' It clearly distinguishes itself from siblings by contrasting with deep_search, web_search, and fetch, emphasizing that it synthesizes across multiple sources rather than retrieving one result.

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 explicit when-to-use guidance with concrete examples (open/analytical, contested, multi-part) and explicit when-not-to-use instructions ('Do NOT use it for a quick fact (web_search), a known page (fetch), or a question one search settles (deep_search)'). It also mentions cost and latency trade-offs, helping the agent select it appropriately.

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