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

Beyond the readOnlyHint and idempotentHint annotations, the description discloses the account/paid-tier requirement, the findings packet shape, gaps[] and contradictions[] behavior, citation_uri fetchability, semantic excerpting, and latency. It does not contradict the annotations; it substantially supplements them.

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 and front-loaded with the account requirement, core purpose, and key alternative. Each sentence earns its place, though the length is at the upper end; the structure is clear enough that nothing feels 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?

For a complex tool with no output schema, the description is remarkably complete: it covers prerequisites, use cases, exclusions, depth semantics, return envelope, citation behavior, and expected latency. An agent has everything needed to invoke it correctly and interpret the results.

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?

Although the schema already covers both parameters, the description adds real semantic value by explaining what each depth value does—quick, standard with gap recovery and contradiction scanning, thorough with lead-chasing—and by clarifying that 'question' accepts broad/multi-part natural language. This goes well beyond the schema.

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 specific action—'grounded multi-source research across Pipeworx's 1497 STRUCTURED data sources'—and clearly distinguishes this tool from open-web search and from ask_pipeworx. It also names sibling alternatives and the conditions for choosing them, so an agent can tell them apart without inspecting schemas.

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 says when to use deep_research ('Best for broad/multi-part questions over structured data'), when to use ask_pipeworx instead ('a single lookup', 'BREAKING or colloquial CURRENT-NEWS'), and what to do if the user is not signed in. This is model guidance: it names alternatives and gives concrete selection rules.

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