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

Annotations already mark the tool as read-only, open-world, and idempotent; the description adds account requirements, paid depth tier, parallel decomposition, response contents (verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[]), non-hallucination behavior, semantic excerpting, and expected latency. This goes well beyond what annotations alone provide and is consistent with them.

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 contains abundant useful detail and is front-loaded with account prerequisites, but the central 'Grounded multi-source research...' passage is repeated nearly verbatim many times, making the text severely bloated and hard to parse. This repetition violates the expectation that every sentence earns its place.

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?

Even without an output schema, the description explains the return packet format: evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[], and contradictions[]. It also covers citation fetchability, excerpting behavior, auth, latency, and data-source boundaries, so an agent has enough context to invoke and interpret 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?

The input schema already documents both parameters with 100% coverage, so the baseline is 3. The description adds meaningful depth semantics: what each depth value means in terms of facets, hops, gap recovery, and contradiction scanning, and it clarifies that the question should be natural language and may be broad or multi-part.

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 identifies a specific action and resource: grounded multi-source research across Pipeworx's 1,517 structured data sources in one call, with a clear negative boundary: 'this is NOT open-web search.' It also contrasts with ask_pipeworx for single lookups, so an agent can distinguish it from sibling tools without needing to open their schemas.

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 or multi-part questions over structured data, and explicit when-not-to-use guidance: single lookups should go to ask_pipeworx instead. It also adds auth routing: if not signed in, use ask_pipeworx because it works on every tier, and clarifies that the 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.