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

The description richly discloses behavior beyond the annotations: account/paid-plan requirements, parallel facet decomposition, explicit gaps[] that are never invented, citation_uri only when fetchable, contradictions[] for standard/thorough, semantic excerpting, and latency expectations. There is no contradiction with the annotations, which only cover read-only and non-destructive characteristics.

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?

Despite being long, the description is dense and every sentence earns its place. It is front-loaded with a critical prerequisite (account required), then covers mechanism, use cases, alternatives, output semantics, and latency in a logical flow. No filler or redundant restatement of the tool name or title.

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 there is no output schema, the description thoroughly explains the findings packet shape, gaps, contradictions, citations, hop field, and edge-case behavior for non-catalog topics. It also covers authentication, cost, latency, and sibling routing, making the description complete enough for an agent to select and 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?

The schema already documents both parameters with 100% coverage, so the baseline is 3. The description adds meaningful enrichment by explaining behavior per depth value ('standard' re-angles unanswered gaps, 'thorough' chases leads) and by clarifying that the question parameter naturally supports broad/multi-part input. This goes beyond the schema's definitions.

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 verb and resource: 'Grounded multi-source research across Pipeworx's 1506 STRUCTURED data sources' in ONE call, explicitly distinguishing it from open-web search. It also names the intended use case with concrete examples ('compare X and Y's regulatory + financial exposure'), making the tool's purpose unmistakable and differentiated from siblings like ask_pipeworx.

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 routing guidance: 'If you are not signed in, use ask_pipeworx instead' and 'For a single lookup use ask_pipeworx'. It also states when deep_research is best ('broad/multi-part questions over structured data') and covers depth-tier behavior so an agent can choose the right depth. This is exemplary when-to-use versus alternatives guidance.

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