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

The description goes far beyond the annotations: it discloses account/plan requirements, parallel routing across 5,743 tools, the findings-packet format, explicit gaps[] instead of invented answers, contradictions[] for standard/thorough, citation fetchability guarantees, semantic excerpting, and expected latency. This is rich behavioral context that annotations alone would not convey.

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 sentence carries substantive guidance: auth, routing, mechanics, output contract, edge cases, and latency. It is somewhat dense and run-on, and the account warning is front-loaded before the core purpose, but the information density justifies the length for a tool of this complexity.

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?

With no output schema, the description fully covers what the agent receives: verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, hop field, gaps[], and contradictions[]. It also covers tool selection boundaries, auth prerequisites, latency expectations, and second-hop behavior, so an agent has everything needed to invoke and interpret this 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 covers 100% of parameters with detailed descriptions, so the baseline is 3. The description adds meaningful depth semantics beyond the schema: depth:'thorough' is paid, 'standard' adds gap recovery, 'thorough' adds lead-chasing, and both add contradiction scanning. It also clarifies that broad natural-language questions are appropriate, which helps an agent choose values confidently.

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 names a concrete operation ('Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources') and explicitly differentiates it from open-web search and from ask_pipeworx. It also states the tool decomposes questions and returns a findings packet, so an agent understands what this tool uniquely produces.

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 gives explicit routing guidance: use for broad/multi-part questions over structured data, use ask_pipeworx for single lookups, and prefer ask_pipeworx for breaking or colloquial current-news topics. It also specifies account requirements and directs unsigned-in users to ask_pipeworx, leaving no ambiguity about when this tool should be selected.

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