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

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior, and the description adds substantial context: account/auth requirements, paid depth tier, parallel decomposition, gap recovery hops, contradictions[], never-invented gaps, stable resolvable citations, semantic excerpting, and latency expectations. No contradiction with annotations exists.

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 largely front-loaded, leading with account requirements and the core one-call research value. Some redundancy exists — ask_pipeworx is pointed to twice — but the structure still reads clearly and every paragraph adds meaningful operational detail.

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 thoroughly explains the return packet, including verbatim evidence, confidence, source, fetched_at, citations, gaps[], contradictions[], and hop fields. It also covers auth, rate/time expectations, error behavior, and scope limits, leaving little an agent needs to infer.

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 value by explaining the behavioral differences between depth levels, giving example research questions, and clarifying that 'question' should be natural language and can be broad or multi-part. It reinforces and enriches the schema without replacing it.

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 over Pipeworx's 1517 structured data sources in one call. It explicitly distinguishes itself from open-web search and from ask_pipeworx, so an agent can tell them apart without opening schemas.

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?

It gives explicit when-to-use guidance: best for broad and multi-part questions over structured data. It also gives clear exclusions and alternatives: 'For a single lookup use ask_pipeworx instead' and 'If you are not signed in, use ask_pipeworx instead', which fully routes the agent between siblings.

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