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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds substantial behavioral detail beyond that: parallel routing across 5,798 tools, the findings-packet shape (verbatim evidence, confidence, source, fetched_at, citation), explicit gaps[] for unanswered facets with a never-invented policy, contradictions[] on standard/thorough, the hop field, citation_uri resolvability conditions, semantic excerpting instead of head-truncation, latency expectations, and account/tier requirements. No contradiction with 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?

Critical routing info (account required, use ask_pipeworx instead) is front-loaded, and nearly every sentence carries real information. However, it is a single dense wall of text with quirky editorializing parentheticals ('one call', '(never invented)', 'not head-truncated') that add noise without structure; it could be tightened with clearer breaks between behavior, output, and timing.

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 carries the return-value burden: it specifies the findings packet fields, gaps[], contradictions[], hop field, and citation_uri fetchability condition. It also covers prerequisites (account tiers, signup), timing (15-60s, up to ~90s), and limitations (not open-web search). For a complex research tool, nothing an agent needs to invoke it correctly 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, and the description adds meaning beyond it: it explains depth tiers in operational terms (hop counts, gap-recovery, contradiction scans), flags that 'thorough' requires a paid plan, and clarifies the question parameter with examples of appropriate broad/multi-part questions. This enriches rather than repeats the schema.

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 verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources... in ONE call'. Explicitly distinguishes itself from siblings and open-web search ('this is NOT open-web search'), and names the alternative ask_pipeworx for single lookups. An agent can clearly tell what this tool does and how it differs.

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 guidance: 'Best for broad/multi-part questions over structured data' with concrete examples ('compare X and Y's regulatory + financial exposure'). Gives explicit when-not-to-use and the alternative: 'For a single lookup use ask_pipeworx instead' and 'If you are not signed in, use ask_pipeworx instead'. Nothing is left to inference.

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