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

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description goes far beyond that by disclosing auth requirements, paid tier constraints, parallel decomposition, gap recovery hops, contradictions scanning, citation fetchability guarantees, semantic excerpting behavior, and expected latency ranges. No annotation 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 long and dense, but nearly every clause earns its place by conveying operational constraints, alternatives, or return behavior. It is front-loaded with the most critical gate (account requirement) and the fallback tool. The structure is somewhat sprawling with many parentheticals, making it less crisp than ideal, but still effective.

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 two-parameter tool with no output schema, the description thoroughly covers the return packet (verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[]), latency, auth, tool routing, and failure behavior. An agent has enough context to select and invoke the tool correctly without needing the output schema.

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% for both parameters, but the description adds meaningful depth semantics beyond the schema: it explains what each depth level does operationally (quick=single hop, standard=gap recovery + contradictions, thorough=paid iterative chase), and clarifies that the question can be broad and multi-part because decomposition is the point.

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 names the specific verb and resource: 'grounded multi-source research' across 1,517 structured data sources in one call. It explicitly distinguishes itself from open-web search and from ask_pipeworx for single lookups, making sibling differentiation clear.

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 states explicit when-to-use guidance: best for broad/multi-part questions over structured data, and when not to use it: 'For a single lookup use ask_pipeworx instead.' It also names the alternative for unsigned-in users and notes the paid-tier requirement, providing actionable routing conditions.

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