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

Annotations already declare readOnlyHint=true, openWorldHint=true, and idempotentHint=true, so the safety profile is covered. The description goes well beyond that by disclosing account/paid-plan requirements, parallel decomposition into 5,798 tools, the gaps[] never-invented behavior, citation_uri always being fetchable, contradictions[], hop fields, semantic excerpting, and latency expectations (15-60s, up to ~90s for thorough). 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 front-loaded with the account requirement and the key alternative (ask_pipeworx), then moves through behavior, return shape, and latency. It is dense and long, with some parenthesis-heavy sentences that make parsing harder, but every sentence earns its place for a tool this complex.

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?

Without an output schema, the description fully specifies the return packet: verbatim evidence, confidence, source, fetched_at, stable pipeworx:// citation, gaps[], and contradictions[]. It also covers authentication, depth tiers, timing, and when citations are present. Nothing an agent needs to invoke or interpret the tool 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 coverage is 100% and both parameters already have meaningful descriptions. The tool description adds extra semantics by elaborating on the depth enum values ('standard' adds a gap-recovery hop; 'thorough' chases leads) and by clarifying that question is natural language where 'broad/multi-part is fine — decomposition is the point.' This adds value beyond 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 1517 STRUCTURED data sources... in ONE call.' It explicitly distinguishes the tool from open-web search and from ask_pipeworx by naming the alternative and the condition for using it. The intended use case is concrete: 'Best for broad/multi-part questions over structured data.'

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 explicitly gives when-to-use and when-not-to-use guidance: 'If you are not signed in, use ask_pipeworx instead' and 'For a single lookup use ask_pipeworx instead.' It also names the specific alternative tool and describes the usage context ('Best for broad/multi-part questions over structured data'). This is clear, actionable routing.

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