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

The annotations already indicate read-only, idempotent, open-world, and non-destructive behavior, but the description adds extensive behavioral context: parallel facet decomposition, a findings packet with evidence/confidence/source/fetched_at/citation, explicit gaps[], contradictions[], semantic excerpting, latency expectations, and the guarantee that findings are never invented. This significantly exceeds what the annotations alone communicate.

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 it is information-dense and front-loaded with the most critical operational constraints: account requirement, the fallback to ask_pipeworx, and the structured-data scope. Every section adds needed context for a complex tool. It could be lightly better organized with clearer paragraph breaks, but no sentence is wasted.

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?

There is no output schema, so the description carries the burden of explaining the return value, and it does so thoroughly: findings packet with verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], and hop field. It also covers auth requirements, paid tiers, latency, and the no-fabrication policy. An agent has enough context to invoke it correctly and interpret results.

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%, so the baseline is already high because both question and depth are well-described in the schema. The description adds value by explaining how depth values translate into behavioral differences (facets, gap-recovery hops, contradiction scanning, paid 'thorough') and by clarifying that the question parameter can be broad or multi-part. It strengthens the schema rather than merely repeating it.

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 states a specific verb and resource: grounded multi-source research across Pipeworx's 1,517 STRUCTURED data sources. It explicitly distinguishes itself from open-web search and from ask_pipeworx, noting that it is best for broad/multi-part questions while single lookups should go elsewhere. This makes it easy for an agent to understand the tool's core function and differentiate it from siblings.

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: use it for broad/multi-part research questions over structured data, and use ask_pipeworx for single lookups or when not signed in. It also mentions the paid-plan requirement for depth:'thorough', naming the alternative tier. This leaves little ambiguity about when the tool should be selected.

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