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

The description goes well beyond the annotations by disclosing account requirements, latency expectations, gap handling, contradictions[], citation_uri fetchability, and the fact that findings are never invented. Nothing here contradicts the readOnly, idempotent, or openWorld 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?

The description is long but dense, with almost every sentence carrying operational value: account gate, alternative routing, input scope, output format, latency, and citation guarantees. It is not concise in word count, but it is efficiently front-loaded with the most important usage 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?

Even without an output schema, the description fully specifies what the agent should expect: findings with evidence, confidence, source, fetched_at, a stable citation URI, gaps[], contradictions[], and hop fields. It also covers account prerequisites, latency, and depth semantics, so an agent has nearly everything needed to invoke and interpret the 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 the schema already documents both parameters well. The description adds meaningful extra context by explaining how depth values affect hops, gap recovery, lead chasing, and contradiction scanning, and by clarifying that the question can be broad and multi-part.

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: it performs grounded multi-source research across 1,517 structured data sources, decomposes questions into facets, and returns a findings packet. It also explicitly distinguishes itself from open-web search and single lookups, making its identity unambiguous.

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?

Usage is explicitly clarified with conditions: broad/multi-part questions over structured data are the target use, single lookups should use ask_pipeworx instead, and unsigned-in users are routed to ask_pipeworx. It also explains when deeper depth variants are appropriate and that thorough requires a paid plan.

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