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

Beyond the annotations (readOnly, idempotent, non-destructive, openWorld), the description discloses many important behaviors: it fragments questions into facets, routes to thousands of tools in parallel, returns citations and explicit gaps, never invents findings, may take 15-90 seconds, and semantically excerpts large records. It also mentions account/paid-plan requirements and a fetchable citation_uri guarantee. This is rich behavioral context well beyond what annotations provide.

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 densely informative, covering account requirements, scope, routing mechanics, output structure, gaps, contradictions, latency, and citation guarantees. Most sentences earn their place, though it is slightly front-loaded with auth/tier requirements before the core purpose and would benefit from tighter organization.

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 must explain return values itself, and it does: findings packet with evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], and hop. It also covers error/edge cases like unsigned-in users, unsupported current-news topics, and latency expectations. Nothing an agent needs to invoke and interpret this tool correctly is missing.

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 already documents both parameters with 100% coverage, so the baseline is 3. The description adds meaningful parameter usage context: depth:'thorough' requires a paid plan, standard adds gap recovery, and question is explicitly allowed to be broad. These details help an agent choose parameter values more correctly than the schema alone.

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 names a specific verb and resource: 'grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources' in one call. It also differentiates itself from siblings such as ask_pipeworx by explicitly stating this is not open-web search and that it returns a findings packet. A capable agent can immediately identify what this tool does and how it differs from alternatives.

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 ('best for broad/multi-part questions over structured data') and when-not-to-use guidance: single lookups should use ask_pipeworx, breaking/current-news topics should prefer ask_pipeworx, and unsigned-in users are directed to ask_pipeworx. These exclusions and alternatives leave little to inference.

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