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

Annotations already mark the tool read-only, idempotent, and non-destructive. The description adds substantial operational context beyond those annotations: account/plan requirements, parallel execution across thousands of internal tools, the exact findings-packet shape (verbatim evidence, confidence, source, fetched_at, stable citation), and explicit gaps[] with a never-invent guarantee. This is far more behavioral disclosure than the annotations alone provide.

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 auth gate, then clearly states what the tool is and is not, how it works, what it returns, and when to use it. It is long because the tool is complex, but the information density is high and nearly every clause earns its place; minor tightening of the explanatory tail would make it fully efficient.

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?

There is no output schema, so the description carries the burden of explaining return behavior. It does so thoroughly: findings packet contents, stable citations, gaps[] for unanswered facets, and explicit direction to ask_pipeworx for single lookups. Combined with the detailed input schema and annotations, an agent has everything needed to select and invoke 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 both parameters already have rich descriptions: the question accepts natural language with broad/multi-part queries, and the depth enum explains quick/standard/thorough with facet counts and the paid flag. The tool description adds qualitative context but does not materially extend parameter meaning beyond what the schema already provides, so the high-coverage baseline of 3 is appropriate.

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 action (grounded multi-source research), the exact resource (1517 structured data sources), and the parallel decomposition method across 5,798 tools. It also explicitly distinguishes itself from open-web search and from ask_pipeworx, so an agent can correctly identify this as the broad/multi-part research tool among its 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?

It gives explicit when-to-use guidance: broad/multi-part questions over structured data, with concrete examples. It also gives exclusions and alternatives: single lookups should use ask_pipeworx, and users who are not signed in should use ask_pipeworx because it works on every tier. Account and paid-plan requirements are stated up front.

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