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

The description discloses key behavioral traits beyond the annotations (which are all false): it is asynchronous ('Returns a job_id immediately'), takes 2-5 minutes, and is a multi-step process (plans, searches, reads, writes). It also mentions the underlying system (gpt-researcher, AI NetCafé). It does not mention potential failures or rate limits, but for this tool the disclosed context is substantial.

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 packed with useful information and is front-loaded with purpose. Each sentence serves a function: behavior, workflow, usage guidance, and example. The mention of '29k stars' is mildly extraneous for an AI agent, but it does not detract significantly; it could be trimmed without losing critical guidance.

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 asynchronous nature, 2 parameters, an output schema, and sibling check_job, the description covers the full workflow: what it does, how long it takes, what it returns immediately, how to follow up, and when to use it. The example ties everything together, making it complete for a tool of this complexity.

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% for both parameters, so the baseline is 3. The description adds extra value by instructing to phrase the topic as a question rather than a keyword, and by providing a concrete example. The depth parameter is well-explained in the schema and the description's example reinforces the default.

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 starts with a strong verb+resource: 'Start an autonomous web research task.' It clearly distinguishes this from sibling tools like check_job by explaining it initiates a research process and returns a job_id immediately. The phrase 'real research, not a single model call' further clarifies its unique scope.

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?

Explicit guidance is given: 'Use this when you need sourced, current information rather than what a model already knows.' It also tells the agent to poll check_job for the result, naming the specific alternative/follow-up tool. This gives clear when-to-use and what-to-do-next context.

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