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

Even though readOnlyHint=true is present, the description adds substantial behavioral context: account/auth requirements, paid plan for thorough depth, parallel decomposition across 5,798 tools, gap[] reporting with 'never invented', contradiction[] scans, fetchability guarantees for citations, semantic excerpting of large records, and expected latency. This far exceeds what annotations alone provide.

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 information-dense; every sentence adds a real operational or behavioral fact, from auth requirements to citation resolvability to timing. It is front-loaded with the most critical constraints (account, alternative tool, not open-web search). A small amount of repetition exists, but it does not undermine usability.

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 complex tool with no output schema, the description compensates fully: it explains the findings packet structure, confidence and source fields, citation URIs, gaps[], contradictions[], handling of large records, and latency. An agent has enough context to invoke the tool correctly and interpret its result, even without seeing an 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%, so the baseline is 3, but the description goes beyond the schema by explaining plan implications, hop behavior, gap recovery, contradiction scanning, and latency differences across depth values. It also frames the question parameter as suitable for broad/multi-part queries, which adds useful selection guidance.

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 uses a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' in ONE call. It also explicitly distinguishes itself from open-web search and names what the tool is NOT for ('single lookup'), making it easily differentiable from siblings like ask_pipeworx.

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 usage conditions: if not signed in, use ask_pipeworx; for a single lookup, use ask_pipeworx; for broad/multi-part structured-data questions, use deep_research. It also explains depth-level selection criteria and paid-plan requirements, so an agent knows exactly when this tool is appropriate.

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