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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, and the description adds substantial context beyond that: account/auth requirements, decomposition into facets, parallel tool routing, the exact contents of the findings packet, gaps[] behavior, contradictions[], semantic excerpting, and latency expectations. No contradiction with annotations.

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 dense and well-structured, front-loading the critical account/alternative note before the core behavior. Every sentence adds useful information, though depth-tier details are partially repeated between the opening and the parameter explanation, and the opening paragraph is slightly cluttered. Overall, the length is justified by the tool's complexity.

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?

Despite having no output schema, the description fully explains what the tool returns: verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], and excerpted records. It also covers auth, cost tier, latency, sibling routing, and the exact difference from ask_pipeworx. Nothing critical for an agent to invoke and interpret the 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?

Schema description coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema: it explains the practical implications of depth values (quick=3 single hop, standard adds gap recovery and contradictions, thorough=6 paid iterative hop), clarifies that the question should be natural language and broad/multi-part, and adds latency context tied to depth. This exceeds the baseline.

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 1517 STRUCTURED data sources' in one call. It explicitly distinguishes itself from open-web search and names the output (findings packet with evidence, confidence, source, citation, gaps), making it clear what the tool does and how it differs 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: 'Best for broad/multi-part questions over structured data' and directly points to the alternative: 'For a single lookup use ask_pipeworx instead' and 'If you are not signed in, use ask_pipeworx instead — it works on every tier.' It also explains the depth tiers and when each is appropriate, so an agent can route correctly.

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