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

Beyond the read-only and idempotent annotations, the description discloses account and plan requirements, latency ranges, parallel routing across 5,724 tools, the findings-packet structure, gaps[] behavior, contradictions[], hop fields, resolvable citation URIs, and semantic excerpting. It also warns that uncatalogued topics return mostly empty gaps, which is exactly the kind of behavioral nuance annotations cannot convey.

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; every sentence adds operational value, and the most decision-critical facts (account requirement, alternatives) are front-loaded. Given the tool's complexity, the length is appropriate rather than wasteful, with only minor overlap of the schema's depth descriptions.

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?

The tool has no output schema, yet the description fully specifies inputs, outputs, failure behavior, authentication requirements, cost tiers, latency, and routing to sibling tools. An agent has everything it needs to decide whether to call this tool and to understand what to do with the result.

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 in detail, so the baseline is 3. The description adds practical meaning by explaining what each depth level actually does operationally — gap recovery, lead-chasing, contradiction scanning — and clarifies that broad/multi-part natural language questions are appropriate.

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 action and resource: grounded multi-source research across Pipeworx's 1,497 structured data sources in one call. It explicitly distinguishes itself from open-web search and from ask_pipeworx, and concrete example questions make the tool's purpose unmistakable.

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 and when-not-to-use guidance: best for broad/multi-part structured-data questions, prefer ask_pipeworx for single lookups or breaking/current-news topics, and use ask_pipeworx if not signed in. It even names the alternative tools directly, leaving no ambiguity.

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