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

Annotations declare readOnly/openWorld/idempotent/non-destructive, and the description adds substantial behavioral context beyond that: it decomposes questions into facets, routes to 6,425 tools in parallel, returns gaps[] rather than inventing answers, performs hop-based recovery, detects contradictions, semantically excerpts large records, and reports timing. This is rich disclosure that helps an agent predict side effects and outputs accurately.

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 every sentence carries necessary information: auth requirements, fallback alternatives, core mechanism, output shape, depth behavior, citation resolvability, excerpting, and latency. It is front-loaded with the most critical constraints (account requirement and when not to use the tool), making it efficient despite its length.

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 the tool's complexity, no output schema, and 9 parameters, the description is remarkably complete. It describes the return packet (verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], hop), auth prerequisites, pricing for thorough, when to prefer alternatives, depth semantics, latency, and record-handling behavior. An agent has enough to decide, invoke, and interpret results 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 the question aliases and the depth enum. The description adds meaningful depth semantics — standard does gap recovery, thorough chases best leads and adds contradictions, thorough is paid — and clarifies that broad/multi-part natural-language questions are acceptable. This goes beyond the schema's terse field descriptions.

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 1679 STRUCTURED data sources... in ONE call.' It explicitly distinguishes itself from open-web search and from sibling tools like ask_pipeworx, so an agent can tell exactly what this tool is for.

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 names alternatives with conditions — use ask_pipeworx for single lookups, for breaking/current news, or when not signed in. It also explains depth-level tradeoffs and expected latency, leaving little to inference.

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