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

Beyond the annotations (read-only, idempotent, non-destructive), the description discloses many behavioral traits: account and paid-tier requirements, parallel tool routing, returned evidence fields, explicit gaps[] for unanswered facets, contradictions[] scans, semantic excerpting, citation fetchability, and expected latency. It even states that findings are never invented, which is valuable risk information.

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 front-loaded with the most critical operational constraint (account required) and the key alternative (ask_pipeworx). Each paragraph adds useful context, though some depth details repeat what is already in the schema, preventing a perfect score.

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 research tool with no output schema, the description is remarkably complete: it explains the return packet structure, confidence/source/citation fields, gap handling, contradiction behavior, excerpting, latency, and depth-tier differences. An agent has enough information to know what to expect and how to invoke it 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?

The input schema already covers both parameters fully, including depth's enum semantics and the question's natural-language, broad/multi-part nature, so the baseline is 3. The description adds some operational context such as paid-tier requirements and latency expectations, but it does not materially enrich the parameter meaning beyond what the schema already provides.

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 uses a specific verb and resource: "Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources... in ONE call." It clearly distinguishes itself from open-web search and from the simpler ask_pipeworx sibling, and it names the intended use case: broad/multi-part questions over structured data.

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 and when-not-to-use guidance: use ask_pipeworx if not signed in, and use ask_pipeworx for a single lookup. It also gives concrete example questions that fit the tool's purpose, so an agent can confidently route to this tool versus alternatives.

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