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

The description goes far beyond the annotations, detailing internal decomposition into facets, parallel tool routing, the findings packet structure (verbatim evidence, confidence, source, fetched_at, citation), explicit gaps[], contradiction[] behavior, semantic excerpting, timing expectations, and citation resolvability. This is a wealth of behavioral disclosure that annotations alone could not convey.

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 and dense, but every sentence adds material value — account prerequisites, core functionality, sibling routing, depth semantics, output fields, timing, and citation guarantees. It is front-loaded with the account requirement and core distinction, though some details like the hop field and citation caveat arrive later in the text. It is comprehensive rather than wasteful.

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 takes on full burden of explaining return values and does so thoroughly: findings packet fields, gaps[], contradictions[], hop field, citation_uri semantics, and timing. It also covers failure modes (empty gaps[] for non-catalog topics) and operational prerequisites (account tiers), making it complete for an agent to invoke 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 coverage is 100%, so the baseline is 3, but the description adds meaningful context: it explains the question parameter is natural language and that broad/multi-part questions are 'the point', and clarifies depth semantics ('standard' re-angles gaps, 'thorough' chases leads). This goes beyond merely restating the schema and helps the agent choose depth levels.

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 clearly states a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1497 STRUCTURED data sources' in one call, and explicitly distinguishes itself from open-web search. It also names the sibling it is not ('this is NOT open-web search') and contrasts with ask_pipeworx, making its role unambiguous.

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?

Explicit when-to-use and when-not-to-use guidance is provided: 'Best for broad/multi-part questions over structured data' and 'For a single lookup use ask_pipeworx'. It also gives alternatives for current-news topics and for unsigned-in users, so the agent has clear routing conditions.

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