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

Beyond the readOnly/idempotent annotations, the description adds rich behavioral context: account and plan requirements, parallel decomposition across 5,908 tools, latency expectations (15-60s, up to ~90s), gap reporting with gaps[], contradictions[] in certain depths, semantic excerpting, and a guarantee never to invent findings. None of this contradicts the annotations.

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?

Although long, the description is densely informative and well-structured: account prerequisite first, then what the tool does, then process, then fit, then output details. Every sentence adds decision-relevant information, and the front-loaded structure means an agent can quickly identify the key constraints.

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 carries the full burden of explaining the return packet (verbatim evidence, confidence, source, fetched_at, stable citations, gaps[], contradictions[]) and operational constraints (auth, paid tier, latency, source scope). It also gives concrete example questions, making the tool fully callable by an agent.

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 both parameters fully (100% coverage), so the description doesn't need to restate them. It adds meaningful extra semantics by explaining the practical consequences of depth choices: paid requirement for 'thorough' and the additional gap-recovery/contradiction passes, which helps an agent choose correctly.

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: multi-source grounded research across Pipeworx's 1,540 structured data sources in one call. It explicitly distinguishes itself from open-web search and from the sibling ask_pipeworx for single lookups, so an agent can tell it apart without opening the schema.

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 guidance: best for broad/multi-part questions over structured data, and clearly says to use ask_pipeworx instead for a single lookup or when not signed in. It also flags that the 'thorough' depth requires a paid plan, routing agents away from unsupported calls.

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