start_research

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

Despite no annotations, the description discloses key behaviors: tasks take minutes, waits for results, returns if in time, continues in background on timeout. Does not cover rate limits or auth but adequate for a research tool.

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?

Well structured with paragraphs, front-loaded purpose, then behavior, then parameters. Slightly verbose but every sentence adds value.

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 complexity (async, multiple parameters, sibling tools), the description is comprehensive. It covers usage, behavior, parameters, and fallback mechanism. Output schema exists so return values are not needed in description.

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 0% so description must explain parameters. It does: model (with example and reference to search_models), query (research question/topic), timeout (default 300, max seconds). Adds significant 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?

Clearly states the tool starts a deep research task, submits a query, and distinguishes itself from siblings like check_research by explaining the async behavior and fallback.

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?

Explicitly tells when to use this tool (to start research) and suggests using search_models to find models. Mentions timeout and background continuation with check_research as alternative.

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