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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses output structure, explicit gaps[], never-invented findings, contradictions[], fetchable citation_uri, semantic excerpting, latency estimates, and paid-plan constraints. It also notes that returned citations are always fetchable, which is meaningful behavioral context. No contradiction with annotations is present.

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 and front-loaded with the account and pricing constraint, followed by scope, behavior, output, and alternatives. Some details are repeated from the depth parameter description, and the run-on mid-section is heavier than necessary, but every sentence still carries a useful piece of operational context.

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?

With no output schema, the description fully specifies what the returned findings packet contains: verbatim evidencidence, confidence, source, fetched_at, citation_uri, hops, gaps[], and contradictions[]. It also covers data-source scope, routing behavior, latency, account conditions, and alternative-tool usage, so an agent has the information needed to select and call 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?

Schema description coverage is 100% and the schema already explains question and depth well. The tool description adds extra value by giving concrete example queries, clarifying latency expectations per depth ('up to ~90s' for thorough), and restating which depth levels produce contradition scans and iterative hops. That lifts it modestly above 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 clearly states a specific verb and resource: grounded multi-source research over Pipeworx's 1506 structured data sources in one call, with facet decomposition, parallel routing, and a findings packet as the output. It also explicitly says 'this is NOT open-web search' and names ask_pipeworx as the single-lookup sibling, so an agent can distinguish it 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: it is 'best for broad/multi-part questions over structured data', and it says 'For a single lookup use ask_pipeworx instead'. It also covers account requirements and directs unsigned-in users to ask_pipeworx, which works on every tier, and notes that thorough depth requires a paid plan.

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