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

Annotations already declare the read-only, open-world, idempotent profile, and the description adds significant behavioral context: it returns a findings packet with verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation, plus explicit gaps[] and a guarantee that data is never invented. It also discloses account/pay-tier prerequisites and latency expectations. 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 relatively long but information-dense. It front-loads the account requirement and the primary alternative, then covers core behavior, output structure, and example use cases. Each sentence contributes a distinct fact, and the length is justified by the tool's complexity.

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 burden of explaining return values, and it does thoroughly: findings packet, evidence, confidence, source, fetched_at, citation, gaps[], and the never-invented guarantee. It also covers prerequisites, latency, and scope boundaries, so together with the schema and annotations nothing essential 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 description coverage is 100%, so the schema already fully documents both parameters. The description adds a small extra constraint (thorough depth needs a paid plan) and clarifies the intended use case, but it does not meaningfully expand the semantic meaning of the parameters beyond what the schema provides.

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 states the tool performs grounded multi-source research over Pipeworx's structured data sources in one call and explicitly distinguishes it from open-web search. It provides concrete example questions and names the alternative for single lookups, making the tool's purpose and scope unambiguous.

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 explicitly says deep_research is best for broad/multi-part questions over structured data and that ask_pipeworx should be used for single lookups. It also gives a conditional rule: if not signed in, use ask_pipeworx because it works on every tier. This is clear when-to-use and when-not-to-use guidance with a named alternative.

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