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

Despite annotations already marking readOnlyHint and idempotentHint, the description adds substantial behavioral context: account and paid-tier requirements, latency expectations, gaps[] for unanswered facets, contradictions[], fetchable citation_uri semantics, hop fields, and semantic excerpting. This goes well 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 appropriately dense for a complex tool. It front-loads the account requirement and then covers capability, alternatives, depth behavior, return format, and latency. It could be tightened by removing some redundancy with the enum descriptions, but the structure is logical and scannable.

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, so the description correctly takes on the burden of explaining return behavior: findings packet, verbatim evidence, confidence, source, fetched_at, stable citations, gaps[], contradictions[], and hop fields. Combined with auth, pricing, latency, and sibling routing, an agent has everything needed to invoke it correctly.

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 description coverage is 100%, so the baseline is 3. The description reinforces what the schema already says about depth and question, but adds little that is not already in the input schema; the paid-plan note and latency implications are useful but mostly behavioral rather than parameter-specific.

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-resource pair: grounded multi-source research across Pipeworx's 1497 structured data sources in one call. It also distinguishes itself explicitly from open-web search and names the primary sibling, ask_pipeworx, making selection unambiguous.

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: it is best for broad/multi-part structured-data questions. It also provides clear exclusions and alternatives: use ask_pipeworx for single lookups, breaking news, or colloquial current-news topics, and use ask_pipeworx if not signed in.

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