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

Beyond the readOnly/openWorld/idempotent hints, the description discloses account and paid-plan requirements, parallel facet decomposition, the findings-packet return shape, gaps[], contradictions[], citation fetchability, semantic excerpting, and expected latency. It also states explicitly that findings are never invented, adding important grounding behavior not visible in 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 and well-structured. It opens with the most decision-critical constraints (account, alternative, intended scope), then proceeds into return semantics and performance. Every sentence earns its place; there is no filler or repetition of schema content.

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, but the description compensates fully by describing the findings packet: verbatim evidence, confidence, source, fetched_at, stable pipeworx:// citation, gaps[], and contradictions[]. Combined with auth requirements, alternative tool recommendations, and latency expectations, an agent has everything needed to decide whether and how to call this tool.

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 input schema already covers both parameters at 100%, so the baseline is 3. The description adds meaningful selection context beyond the schema: depth:'thorough' requires a paid plan, standard and thorough add contradiction scans, and latency expectations differ by depth. This helps the agent choose a depth value rather than merely parse the enum.

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 in one call over Pipeworx's structured data sources. It explicitly says this is NOT open-web search and gives concrete examples like 'compare X and Y's regulatory + financial exposure', making the tool's scope easy to distinguish from siblings.

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 when-not-to-use guidance ('For a single lookup use ask_pipeworx instead'). It also provides an access-based alternative: if the user is not signed in, use ask_pipeworx. This is strong routing information.

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