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

Annotations already mark this read-only, idempotent, open-world, and non-destructive; the description goes well beyond them by disclosing the account/paid-plan requirement, 15-90s latency, the gaps[] never-invented guarantee, contradictions[] for standard/thorough, hop fields, the always-resolvable citation_uri guarantee, and semantic excerpting of long records. Nothing contradicts the 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?

Long (~280 words) but nearly every sentence earns its place: auth, fallback, scope, mechanism, response structure, when-to-use, when-not-to-use, depth semantics, and timing. It is front-loaded with the account requirement and core purpose. It loses a point for several dense run-on sentences and mild overlap with the depth parameter's schema description.

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?

With no output schema, the description fully compensates by specifying the findings packet (verbatim evidence, confidence, source, fetched_at, citation), gaps[], contradictions[], hop, citation_uri resolvability, and timing. Auth prerequisites, fallback routing, and input guidance are all covered. Only failure-mode behavior is absent, which is minor for selection and invocation.

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%, so the baseline is 3; the description adds real value by tagging depth:'thorough' as paid-only, attaching latency expectations to depth tiers, explaining the second-hop gap-recovery behavior, and offering natural-language examples for the question parameter. It extends the schema rather than restating 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?

States a precise verb+resource: 'grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources... in ONE call', and explicitly draws boundaries with 'this is NOT open-web search' and 'For a single lookup use ask_pipeworx instead.' An agent can distinguish this from ask_pipeworx and the other 30 siblings without opening another schema.

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?

Gives explicit when-to-use ('Best for broad/multi-part questions over structured data' with two concrete example questions), an explicit when-not-to-use (single lookups), and a named fallback with a condition ('If you are not signed in, use ask_pipeworx instead — it works on every tier'). No usage decision is left to inference.

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