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

The description is exceptionally transparent about behavior: it is grounded, not open-web search, decomposes questions, routes to tools in parallel, returns explicit gaps rather than inventing findings, includes contradictions for certain depths, and explains how citations are guaranteed fetchable. It also discloses latency expectations and the excerpting of large records. This far exceeds what the annotations alone 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 but information-dense, with almost every sentence contributing a distinct behavioral or routing fact. It is front-loaded with the account requirement and the alternative tool. It loses a point because the single-paragraph presentation and heavy use of parenthetical asides make it harder to skim, but it is not padded.

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 that there is no output schema, the description carries the full burden of explaining return values, and it does so thoroughly: findings include verbatim evidence, confidence, source, fetched_at, and a stable citation_uri, plus gaps[] and contradictions[] where applicable. It also covers timing, prerequisites, and behavior differences across depth levels. Nothing essential for calling the tool correctly is missing.

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 schema already covers the two parameters, so the baseline is 3. The description adds meaningful value by explicitly connecting depths to behavior ('thorough' requires a paid plan, standard/thorough add gap recovery and contradictions, thorough adds iterative lead-chasing) and by clarifying that the question parameter accepts broad natural language. This is more than a restatement of 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 clearly identifies the tool as a grounded multi-source research capability over Pipeworx's structured data sources and distinguishes it from open-web search and from ask_pipeworx. It names the resource, the action (decomposes and routes research facets), and the deliverable (findings packet with citations and gaps). No ambiguity remains about 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 explicitly states when to use deep_research: broad/multi-part questions over structured data. It also gives precise when-not-to-use guidance: single lookups should use ask_pipeworx, and users who are not signed in should use ask_pipeworx instead. It additionally names the paid requirement for the 'thorough' depth, making routing decisions concrete.

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