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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses account requirements, paid-plan limits, latency expectations, parallel decomposition, gap recovery behavior, contradictions discovery, semantic excerpting, and the guarantee that gaps are never invented. It also clarifies citation fetchability. These are substantial behavioral details beyond what annotations provide.

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 it is front-loaded with the most critical constraints (account required, alternative for signed-out users) and then flows into mechanism, output, use cases, and caveats. Some depth details are repeated from the schema, and the prose is dense, but nearly every sentence adds practical behavior or selection context.

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 present, the description compensates by thoroughly describing the findings packet, including evidence, confidence, source, fetched_at, stable citations, gaps[], and contradictions[]. It also covers latency, depth tiers, alternatives, and limitations. An agent has enough context to invoke the tool correctly and anticipate its return shape.

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 already strong. The description adds extra parameter semantics by explaining that depth controls the number of facets/hops, that 'thorough' requires a paid plan, and that the question can be broad/multi-part because decomposition is the point. This enriches parameter understanding beyond the schema.

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 action ('Grounded multi-source research'), specifies the resource (Pipeworx's 1500 structured data sources), and clearly differentiates itself from open-web search and from sibling ask_pipeworx. It also explains the mechanism (decompose, route, parallel) so an agent immediately understands 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 gives explicit when-to-use guidance: broad/multi-part questions over structured data, with concrete examples. It also gives a clear exclusion and alternative: 'For a single lookup use ask_pipeworx' and 'If you are not signed in, use ask_pipeworx instead'. This leaves little ambiguity about tool selection.

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