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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds significant behavioral context: it never invents findings (explicit gaps[]), returns verbatim evidence with confidence/source/fetched_at/citation, performs gap recovery hops, returns contradictions[], semantically excerpts large records, and gives latency expectations (15-60s, up to ~90s for thorough). This goes far beyond 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 description is long but information-dense. It front-loads the account requirement and the key distinction from open-web search. Every sentence adds value: auth prerequisite, alternative, source scope, parallelism, output format, gaps, depth semantics, hop field, citation_uri, contradictions, excerpting, and latency. It's verbose but earns its length; slight deduction for a somewhat sprawling sentence structure.

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?

For a complex tool with 2 params, no output schema, and rich annotations, the description covers what an agent needs to invoke it correctly: auth prerequisite, fallback tool, input shape, depth semantics, return packet contents, latency expectations, and limitations. No critical gap is left unaddressed.

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% and the schema already describes depth and question well. The description adds nuance beyond the schema: it explains how depth values translate to hops and gap recovery, and notes that thorough is paid, which the schema enum doesn't convey. It also clarifies what kind of questions work best. Minor deduction because most parameter semantics are already in the schema, but the description adds real value around depth behavior.

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 states this is a grounded multi-source research tool over Pipeworx's 1,540 structured data sources, decomposing questions into facets and routing them in parallel. It explicitly distinguishes itself from open-web search and names what it is NOT. The sibling comparison is clear: it's for broad/multi-part structured-data questions, not single lookups.

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 to use ask_pipeworx instead when not signed in, and for single lookups. It also mentions prefer ask_pipeworx for trending/live news topics since deep_research will return gaps. This is explicit when-to-use and when-not-to-use guidance with named alternatives.

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