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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, but the description adds substantial behavioral context beyond those: the account/paid-plan requirement, parallel decomposition across 5,743 tools, the findings-packet shape with gaps[] and contradictions[], stable citeable pipeworx:// URIs, semantic excerpting of large records, and latency expectations. It discloses that the tool "never invented" data and that unpresent topics yield empty gaps[].

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 deliberately dense and front-loaded with the most critical caveat (account required, paid tier for thorough). Each clause earns its place by describing a distinct behavior or exclusion. Slight redundancy exists between the schema's depth descriptions and those in the text, but for a tool of this complexity the length is justified.

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 must carry the full burden of explaining return values, and it does: verbatim evidence, confidence, source, fetched_at, a stable pipeworx:// citation per finding, gaps[], contradictions[], and the hop field. It also covers prerequisites, latency, and fallback paths, leaving nothing an agent needs to decide correct invocation or interpret results.

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 meaningful context by explaining what "broad/multi-part" questions are appropriate, clarifying that quick/standard/thorough differ in facets, hops, and contradiction scanning, and explicitly tying depth to timing. It reinforces the schema's enum semantics rather than repeating them verbatim, earning an above-baseline score.

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 states a specific verb and resource: "Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources... in ONE call." It clearly distinguishes itself from siblings with "this is NOT open-web search" and names ask_pipeworx as the alternative for different use cases. An agent can immediately understand the tool's unique role among the 34 sibling tools.

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 gives explicit when-to-use and when-not-to-use guidance: "If you are not signed in, use ask_pipeworx instead", "For a single lookup use ask_pipeworx", and "For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx." It also explains how depth levels map to different investigation strategies, making the decision process concrete for the agent.

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