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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, and the description adds substantial behavioral detail without contradicting them: it returns explicit gaps[], never invents findings, provides stable pipeworx:// citations, runs parallel tool routing, performs gap-recovery hops, flags contradictions, and states latency expectations. This is far beyond what annotations alone 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 dense and front-loaded with the most critical operational facts (account requirement, alternative tool, core capability). Nearly every sentence adds value, though some details—such as the 5,798 tools count and internal routing mechanics—are more elaborate than strictly necessary, and a few depth details repeat the schema.

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?

With no output schema, the description fully compensates by explaining the findings packet structure: verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], and semantic excerpting. It also covers auth, pricing, latency, and alternative routing. For a complex multi-source research tool, nothing essential is missing.

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. The description adds useful context beyond the schema: the paid-plan constraint for 'thorough', expected latency ranges per depth, and examples of appropriate question phrasing. It slightly overlaps with the schema's depth description but still enriches agent understanding of how to choose depth.

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?

States a specific action and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources in ONE call.' Explicitly distinguishes itself from open-web search and names ask_pipeworx as the alternative for simpler cases. The examples ('compare X and Y's regulatory + financial exposure') make the purpose concrete.

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?

Gives explicit when-to-use and when-not-to-use guidance: 'Best for broad/multi-part questions over structured data' and 'For a single lookup use ask_pipeworx instead.' Also provides auth/tier conditions: 'If you are not signed in, use ask_pipeworx instead — it works on every tier' and notes thorough requires a paid plan. No ambiguity about routing.

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