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

Annotations already mark the tool read-only and idempotent, and the description adds substantial behavior beyond that: account/paid-plan requirements, 15-90s latency, gaps[] for unanswered facets, contradictions[] for standard/thorough, semantic excerpting of large records, and fetchable pipeworx:// citations. No statement contradicts the 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?

The main text is well front-loaded and dense, but the description is long and is followed by a redundant repeated passage beginning 'Return a findings packet...', plus a malformed aside '(rest? Actually there's an apparent contamination):' if treated as part of the definition. That reduces structural quality even though the first portion is efficient.

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?

Despite having no output schema, the description tells the agent exactly what a findings packet contains (evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[]) and covers prerequisites, alternatives, latency, and behavior across depth levels. Nothing needed 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 schema already documents both parameters. The description adds meaningful depth-specific semantics (thorough requires a paid plan, returns contradictions, ~90s; standard also returns contradictions) and clarifies that question is natural-language and multi-part. This exceeds the baseline but doesn't need to carry the whole burden.

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 names a specific action ('Grounded multi-source research'), a resource ('Pipeworx's 1517 STRUCTURED data sources'), and a mechanism (decomposes into facets, routes to 5,798 tools in parallel). It explicitly contrasts with open-web search and with ask_pipeworx, so an agent can distinguish it from siblings without opening the schema.

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?

It gives concrete conditions: use for broad/multi-part questions over structured data, with two examples; use ask_pipeworx for a single lookup or if not signed in. It also states depth tiers and paid/unpaid access constraints, making the when-to-use decision explicit.

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