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

The description goes far beyond the readOnly/openWorld/idempotent annotations. It discloses account and payment requirements, the response format (findings packet with verbatim evidence, confidence, source, fetched_at, citation), the explicit gaps[] for unanswered facets (never invented), contradictions[] available in standard/thorough, semantic excerpting behavior, and expected latency (15-60s, up to ~90s). It also explains the conditional nature of citation_uri, ensuring the agent knows citations are only present when they are fetchable. This provides substantial behavioral context beyond 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 long but information-dense, and the most critical operational facts (account required, free tier, paid thorough, alternative tool) are front-loaded. Every sentence contributes useful behavior or usage detail. It could be tightened slightly, but for a tool with this complexity, the length is justified and the structure is clear.

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?

Given there is no output schema, the description must convey return expectations, and it does so thoroughly: findings packet structure, citations, gaps[], contradictions[], hop field, semantic excerpting, and timing. It also covers prerequisites (sign-in, paid plan), alternatives, and edge cases (citations only when fetchable). An agent has enough context to select and confidently invoke this tool without an output schema.

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?

The input schema already covers 100% of parameters with detailed descriptions, including the depth enum's quick/standard/thorough semantics. The description adds usage context (e.g., broad questions are appropriate) and reiterates that thorough requires a paid plan, but that information is also present in the schema. Since schema_description_coverage is 100%, a baseline of 3 is appropriate; the description does not materially extend parameter-level understanding.

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 begins with a clear statement of what the tool does: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources ... in ONE call'. It names the resource (structured data sources), the action (multi-source research with decomposition and parallel routing), and explicitly contrasts it with 'open-web search'. It further distinguishes itself by naming the sibling tool ask_pipeworx as the alternative for single lookups, so an agent can easily differentiate them.

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' guidance: 'Best for broad/multi-part questions over structured data' and contrasts with 'For a single lookup use ask_pipeworx instead'. It also states when not to use the tool: 'If you are not signed in, use ask_pipeworx instead — it works on every tier'. This is highly actionable guidance that names an alternative and the condition that selects it.

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