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

Even with readOnlyHint=true, the description goes far beyond the annotations: it discloses account requirements, the paid tier for 'thorough', that results are never invented and include explicit gaps[], that contradictions[] appear for 'standard' and 'thorough', how citations are guaranteed fetchable, semantic excerpting behavior, and expected latency. No annotation contradiction 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 long but every sentence earns its place: it front-loads the account requirement and the most critical distinction (not open-web search), then layers core behavior, alternatives, return packet, depth semantics, and latency. Information is grouped logically, and nothing is redundant.

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?

Because there is no output schema, the description carries the full burden of explaining return values, and it does: findings packet fields (evidence, confidence, source, fetched_at, citation_uri), gaps[], contradictions[], hop field, and fetchability guarantees. It also covers auth, iteration, excerpting, and latency, so an agent has everything required to invoke and interpret the tool correctly.

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?

With 100% schema coverage, the baseline is already met, and the description still adds substantial meaning: it explains what each depth value does (quick as single-hop, standard as gap-recovery plus contradictions, thorough as lead-chasing plus contradictions), the paid requirement for 'thorough', and that the question field accepts multi-part natural-language questions.

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 identifies a specific verb ('research') and resource ('Pipeworx's 1517 STRUCTURED data sources'), explicitly distinguishes itself from open-web search ('this is NOT open-web search'), and names the sibling it is not ('For a single lookup use ask_pipeworx instead'). An agent can determine exactly what this tool is for without inspecting the schema.

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?

It gives explicit when-to-use guidance: 'Best for broad/multi-part questions over structured data' with concrete examples, and an explicit exclusion: 'For a single lookup use ask_pipeworx instead.' It also handles auth routing: 'If you are not signed in, use ask_pipeworx instead — it works on every tier,' covering both access and alternative selection.

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