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

The description goes far beyond the annotations: it discloses account/auth requirements, parallel decomposition behavior, latency expectations (15-60s, up to ~90s), gap reporting with explicit gaps[], contradiction scanning, semantic excerpting, and that citations are only present when actually resolvable. Annotations declare readOnlyHint and idempotentHint, which the description's research behavior aligns with. 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 information-dense and appropriately sized for a complex tool. It is front-loaded with the most critical operational facts: account requirement, signed-out fallback, and the core value proposition. While it could be broken into clearer paragraphs, every sentence contributes practical details about behavior, output, or constraints.

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 carries the full burden of explaining return values, and it does so thoroughly: findings packet contents, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], hop field, and excerpting behavior. Prerequisites, pricing, latency, and alternative routing are all covered. An agent has enough context to decide whether to call this tool and what to expect back.

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?

The input schema already covers both parameters in detail, and the description reinforces them. It adds meaning beyond the schema by tying depth levels to specific behaviors (gap recovery, lead chasing, contradiction scan) and by explaining that 'thorough' requires a paid plan. The schema description is strong, so the tool description's additional context is a bonus rather than a requirement.

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 names a specific verb and resource: grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources in one call. It distinguishes itself from siblings by explicitly saying this is NOT open-web search and by directing single-lookup questions to ask_pipeworx instead. The examples ('compare X and Y's regulatory + financial exposure', 'research the filings + market picture for ACME') further pin down the intended purpose.

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?

Usage guidance is explicit and actionable: broad/multi-part questions over structured data are the right use case, while single lookups should use ask_pipeworx. It also states the account requirement and that the 'thorough' depth needs a paid plan, routing unsigned users to ask_pipeworx instead. This gives an agent both inclusion and exclusion criteria.

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