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

This is exceptionally transparent. It discloses that the tool is NOT open-web search, that answers are never invented and gaps[] are explicit, that citations are only present when fetchable, that records are semantically excerpted, that depth variants add hops and contradiction scans, and expected latency. Annotations already signal readOnly/openWorld/idempotent, and the description complements rather than contradicts them.

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 dense but every sentence carries unique operational value: auth, fallback, data scope, execution model, return packet contents, limitations, latency. It is not tautological and front-loads the account requirement before the substantive behavior. It is longer than average, but the tool is genuinely complex and the length is warranted.

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?

Given the tool's complexity, the description covers account prerequisites, alternative routing, facet decomposition, parallel execution, output structure (evidence/confidence/source/fetched_at/citation), failure modes (gaps[], contradictions[]), excerpting behavior, and latency expectations. There is no output schema, so the description bears full responsibility for explaining the return packet, and it does so thoroughly.

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 baseline is 3. The description adds significant semantic depth beyond the schema by explaining what depth levels actually do in behavioral terms (gap recovery, contradiction scan, lead chasing) and by clarifying that question supports broad/multi-part natural language. It slightly falls short of 5 because the depth mechanics could be more explicitly tied to cost/time tradeoffs, but the added context is strong.

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 clearly defines the tool as grounded multi-source research over Pipeworx's 1500 structured data sources, distinguishing it from open-web search and from ask_pipeworx for live news. It names concrete use cases ('compare X and Y's regulatory + financial exposure') and states it decomposes questions into facets and routes to parallel tools.

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 explicitly says when to use it (broad/multi-part questions over structured data) and when not to (single lookups, breaking/current topics) with a named alternative: ask_pipeworx. It also warns that unrelated topics return empty gaps[] and explains the account/depth prerequisite with the fallback.

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