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

Annotations already mark the tool as readOnly, idempotent, and non-destructive, and the description does not contradict them. It goes well beyond annotations by disclosing decomposition mechanics, parallel routing, the findings packet format, citation_uri fetchability, gaps[], contradictions[], semantic excerpting behavior, latency expectations, and authentication/paywall constraints.

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 densely informative, and key facts are front-loaded: account requirement, fallback tool, scope, mechanism, output format, depth options, and latency. Every sentence adds operational value, and the organization makes it easy for an agent to extract the decision-relevant details in order.

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?

There is no output schema, so the description carries the full burden of explaining return values, and it does so thoroughly: verbatim evidence, confidence, source, fetched_at, citation_uri, gaps, contradictions, and hop field. It also covers prerequisites, latency, and failure-recovery behavior, leaving no critical gap for an agent that must 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 this is already a strong baseline. The description adds meaningful context beyond the schema by explaining what kinds of questions suit the 'question' parameter and by clarifying that depth:'thorough' requires a paid plan, while also illustrating the behavioral differences between quick, standard, and thorough.

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: grounded multi-source research across Pipeworx's structured data sources, with decomposition and parallel routing to 5,798 tools. It explicitly distinguishes itself from open-web search and from sibling ask_pipeworx, so an agent can differentiate it 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?

It gives explicit when-to-use guidance: best for broad/multi-part structured-data questions, and for a single lookup use ask_pipeworx instead. It also names the fallback for unsigned users and calls out that depth:'thorough' requires a paid plan, so an agent can decide whether the tool is available and appropriate.

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