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

Annotations declare readOnly/openWorld/idempotent/non-destructive, and the description adds substantial context beyond them: account and paid-tier requirements, 15-90s latency expectations, the findings-packet shape, the never-invented gaps[] guarantee, contradictions[] on standard/thorough, semantic excerpting rather than head-truncation, and citation_uri resolvability. All disclosed behaviors are consistent with the annotations — nothing contradicts 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 most important facts are front-loaded (account requirement, non-open-web scope, when to use vs ask_pipeworx), but the description is long and contains a garbled/corrupted fragment mid-way — '(One request per call — ONE pass through. Next: come back for multi-part or anything that needs TWO passes: ...' — that breaks coherence. Depth semantics are also re-explained in prose, partially duplicating the schema's depth description, which adds length without new information.

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?

For a complex tool with no output schema, the description carries the return-format burden fully: findings packet fields, gaps[], contradictions[], hop field, citation URIs, latency ranges, auth/tier prerequisites, and the not-open-web-search scoping. The garbled fragment is a recoverable blemish — the depth behavior it gestures at is fully covered both in the schema and in the surrounding prose — so no essential coverage gap remains.

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 baseline is 3; the description adds meaning beyond the schema by coupling 'thorough' to a paid plan (absent from the schema) and by mapping each depth value to observable behavior (single hop vs gap-recovery vs iterative lead-chasing) that complements the schema's own enum descriptions. It doesn't fully transform the parameters, but it clearly adds value.

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 specific verb+resource+scope: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources ... in ONE call', and explicitly contrasts with 'this is NOT open-web search.' It distinguishes cleanly from sibling ask_pipeworx by scope (broad/multi-part research vs single lookup) and from the many other data-lookup siblings like get_flights_in_area or entity_profile.

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 condition-based routing: 'If you are not signed in, use ask_pipeworx instead — it works on every tier' and 'For a single lookup use ask_pipeworx.' Affirmative guidance is concrete too: 'Best for broad/multi-part questions over structured data' with two worked example phrasings. An agent knows exactly when to pick this tool and when to pick the alternative.

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