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

The annotations are read-only/idempotent/open-world, and the description goes well beyond them: it discloses the auth/paid requirement, 15-90s latency, gap[] behavior, contradictions[], fetchable citation_uri guarantee, and semantic excerpting of large records. This gives the agent a rich behavioral model without contradicting any annotation.

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 dense and long, but each sentence carries unique information: auth, alternative, core mechanism, output packet, depth semantics, latency, citation guarantees. It is front-loaded with the most critical routing and auth info. A minor deduction for the wall-of-text formatting and slight redundancy in elaboration, but it remains appropriately sized for a complex tool.

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 takes on the burden of explaining return values—and it does: findings packet with verbatim evidence, confidence, source, fetched_at, citation, gaps[], contradictions[], hop field, citation_uri. It also covers auth, latency, alternative routing, and depth behavior. An agent has everything needed to invoke this tool 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?

Schema coverage is 100%, so baseline is 3. The description adds value by tying the depth parameter to paid tier and latency expectations, and clarifying that the question parameter accepts broad/multi-part natural language. It doesn't repeat schema internals but adds operational context.

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+resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' in ONE call, and clearly distinguishes itself from open-web search and sibling ask_pipeworx. It also explains the decomposition and parallel routing behavior, leaving no doubt about what the tool uniquely 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?

Explicitly says 'Best for broad/multi-part questions over structured data' and 'For a single lookup use ask_pipeworx instead', plus an auth-based routing rule: if not signed in, use ask_pipeworx. It also explains when to pick depth levels, giving clear when-to-use and when-not-to-use guidance.

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