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

Annotations provide readOnly/openWorld/idempotent/destructive hints, but the description adds rich behavioral context: account and paid-plan requirements, 'this is NOT open-web search,' explicit gaps[] never invented, contradiction scanning, semantic excerpting of large records, citation_uri fetchability guarantee, and latency expectations. It fully discloses the operational traits an agent needs to predict behavior.

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 long but almost every sentence carries distinct information—auth, alternatives, mechanism, output, use cases, depth semantics, citation behavior, latency. It is appropriately front-loaded with the account requirement and core purpose. A small amount of repetition around 'use ask_pipeworx' could be trimmed, but overall it earns its length for a complex tool.

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 explains the return value: findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[], contradictions[], hop field, and citation_uri. It also covers auth, when-not-to-use, depth semantics, and timing. No critical operational detail is left to inference.

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?

Although schema coverage is 100%, the description adds substantial meaning beyond the schema. It details the depth levels in terms of hops and paid tiers ('thorough' needs a paid plan), clarifies that the question can be broad/multi-part, and explains what each depth returns (gap recovery, contradictions, iterative chasing). This goes well beyond the baseline of 3.

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 uses a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1498 STRUCTURED data sources... in ONE call.' It clearly distinguishes itself from open-web search and from sibling ask_pipeworx by explaining its facet-decomposition and parallel-routing mechanism. The scope and output are both concretely defined, leaving no ambiguity about what the tool does.

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?

The description explicitly names alternatives and the conditions that select them: 'For a single lookup use ask_pipeworx,' 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx,' and 'If you are not signed in, use ask_pipeworx instead.' It also gives concrete examples of the best use case ('compare X and Y's regulatory + financial exposure'). This is exceptional routing guidance.

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