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

Despite readOnlyHint, openWorldHint, and idempotentHint annotations, the description adds substantial behavioral context: like not inventing findings, returning gaps[], contradictions[], hop fields, citation_uri availability, semantic excerpting, estimated latency ranges, account/depth requirements, and parallel routing mechanics. This goes well beyond the annotations and gives the agent a realistic model of what the tool does internally.

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 and information-rich, but it is long and slightly meandering; the first sentence packs several concepts including a URL and plan details, followed by multiple parentheticals and em-dash clauses. It front-loads the most important facts well, but the sentence flow is somewhat convoluted (e.g., 'not head-truncated', 'so a citation you get back is always fetchable'). Every sentence earns its place, but structure could be tightened.

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 that there is no output schema, the description compensates thoroughly by describing what a findings packet includes (verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], hop field). It also covers failure behavior ('never invented', 'gaps[]'), latency expectations, authentication, tier limitations, and input format. This is a complete picture for an agent to invoke the tool 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 coverage is 100%, so both parameters are already documented; however, the description significantly enhances the depth parameter semantics by explaining the practical meaning of quick/standard/thorough: number of facets, single-hop vs gap-recovery vs iterative chasing, contradiction scanning, and paid status. This adds real decision-making value beyond the enum descriptions. The question parameter is contextually explained as supporting broad/multi-part decomposition, which is the tool's core purpose.

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 the tool's specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources... in ONE call'. It explicitly distinguishes from open-web search and names a sibling alternative, ask_pipeworx, making it easy for an agent to understand when to select this tool. The examples of broad questions further clarify intended use.

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 provides explicit sign-in requirements and alternative guidance: 'ACCOUNT REQUIRED... If you are not signed in, use ask_pipeworx instead'. It also contrasts with open-web search and suggests the best use case: 'Best for broad/multi-part questions over structured data... For a single lookup use ask_pipeworx.' This gives clear when-to-use and when-not-to-use framing.

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