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

Annotations already declare read-only, safe, idempotent behavior, and the description adds meaningful nuance: decomposition into facets, parallel routing to 5,743 tools, findings packet shape, gaps[] honesty guarantees, hop field, contradictions[], semantic excerpting, and expected latency. It does not dive into every detail (e.g., pagination, error modes), but for a research tool this is strong behavioral disclosure.

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 dense but organized: access constraint first, then what it is, when to use it, depth semantics, output guarantees, and latency. It earns its length by replacing what an agent would otherwise have to infer from five tool calls. A lighter organization/headings would make it even more scannable, but every sentence adds decision-relevant information.

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 no output schema, the description supplies the return contract (findings packet with verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], hop, contradictions[]) — enough for an agent to invoke and reason about results. It also covers account/depth constraints and latency expectations. It could mention exact output shape or error conditions, but overall the agent has what it needs to call this 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?

Schema coverage is 100%, so the schema already documents depth and question. The description adds value by explaining what each depth choice produces (quick=3 facets/hop, standard=gap recovery + contradictions, thorough=iterative chase + contradictions) and by clarifying that multi-part natural language questions are the intended input. It doesn't repeat schema facts verbatim, which is the right balance.

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 opens by naming the tool's scope and core behavior: grounded multi-source research across Pipeworx's 1500 structured data sources in one call, explicitly distinguishing it from open-web search. It lists concrete example questions and source categories, and the sibling comparison makes its role unmistakable.

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 routing direction: if unsigned, use ask_pipeworx; for single lookups use ask_pipeworx; for broad/multi-part structured-data questions use deep_research. It also distinguishes from open-web/current-news cases, telling agents to prefer ask_pipeworx for BREAKING topics. It even addresses second-hop behavior and depth choices, which clarifies exactly when this tool is the right pick.

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