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

The description goes far beyond annotations: it discloses account requirements, paid tier limits, parallel decomposition, output shape, explicit gaps for unanswered facets, contradiction reporting, citation fetchability guarantees, semantic excerpting, and expected latency. Since annotations already mark it read-only, idempotent, and non-destructive, no contradiction 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 densely packed with useful information and front-loads the most critical constraints (account requirement, alternative tool) before diving into mechanics. It is long, but the length is largely justified by the tool's complexity; minor redundancy around contradictions and hops keeps it from a perfect 5.

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?

For a complex tool with no output schema, the description is exceptionally complete: it covers prerequisites, alternatives, input semantics, expected output, corner cases like unanswered gaps, citation resolvability, and runtime expectations. An agent has enough context to decide whether to call this tool and what to expect afterward.

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 meaningful context beyond the schema, especially for the depth parameter: it explains the paid requirement for thorough and the practical impact on latency and follow-up hops. This makes the parameters more usable without the agent needing to infer behavior.

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 the tool performs grounded multi-source research across Pipeworx's structured data sources in one call, and explicitly distinguishes it from open-web search. It also contrasts it with ask_pipeworx, making the tool's unique role among siblings evident.

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 guidance on when to use deep_research: broad/multi-part questions over structured data, and when not to: single lookups should use ask_pipeworx. It even directs unsigned-in users to ask_pipeworx instead, which is actionable routing information beyond the schema.

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