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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses account/paywall gating, parallel decomposition across tools, the explicit gaps[] never-invented guarantee, contradictions[] behavior for certain depths, citation fetchability semantics, semantic excerpting, and latency expectations. These are substantive behavioral facts an agent needs and are not present in annotations. No contradiction found.

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 every sentence adds distinct operational value: account requirement, fallback tool, core behavior, output packet fields, depth variants, contradiction scans, excerpting, and latency. It is front-loaded with the most critical prerequisite and contains no filler or redundancy.

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?

Despite having no output schema, the description enumerates the findings packet contents (verbatim evidence, confidence, source, fetched_at, stable citation), the gaps[] field, contradictions[], the hop field, citation_uri fetchability, and latency ranges. Combined with full schema coverage for the two parameters, an agent has everything needed to call and interpret the tool correctly.

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%, and the schema already thoroughly describes both 'question' and 'depth', including enum values, defaults, and hop behavior. The tool description adds contextual routing and depth-specific caveats but no new parameter-level semantics. Therefore the baseline of 3 applies.

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 names a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources'. It explicitly differentiates itself from alternatives: 'this is NOT open-web search' and 'For a single lookup use ask_pipeworx instead.' This gives an agent a precise, unambiguous idea of what the tool does and how it differs from siblings.

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?

Provides explicit when-to-use guidance: 'Best for broad/multi-part questions over structured data' with concrete examples. It also states when not to use it and which alternative to choose: 'If you are not signed in, use ask_pipeworx instead' and 'For a single lookup use ask_pipeworx.' This is clear routing with no inference required.

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