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

With readOnly, openWorld, and idempotent annotations, the description adds substantial behavioral detail: it decomposes questions into facets, routes to tools in parallel, never invents answers, includes gaps[], returns contradictions[] for standard/thorough depths, retries unanswered facets, and cites fetchable pipeworx:// URIs only when resolvable. It also discloses account tier requirements and latency expectations. No contradictions 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?

The description is long, but every section earns its place: access prerequisites, core distinction, process, output format, gap handling, depth semantics, citation guarantees, and latency. It is front-loaded with the most critical operational constraint (account required). The only minor cost is density, but no content is redundant.

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 fully carries the burden of explaining return values. It does so thoroughly: findings packet with verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], and hop field. It also covers prerequisites, latency, and edge cases like non-fetchable citations. An agent has everything needed to call and interpret the result.

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?

Though the schema already documents both parameters, the description significantly enriches them: it explains that 'question' is expected to be broad/multi-part and natural language, and it details what each depth value ('quick', 'standard', 'thorough') does in terms of hops, gap recovery, contradictions, and paid access. This goes well beyond the schema's enum descriptions.

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 states a precise verb and resource: it conducts 'grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' in one call. It explicitly distinguishes itself from open-web search and from ask_pipeworx for single lookups. The clear focus on broad/multi-part structured-data questions makes its purpose 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?

It explicitly tells agents when to use this tool vs alternatives: 'If you are not signed in, use ask_pipeworx instead' and 'For a single lookup use ask_pipeworx.' It also specifies the best-fit scenario: broad/multi-part questions over structured data. This is exemplary routing guidance.

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