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

Annotations only declare readOnly/openWorld/idempotent/non-destructive, but the description adds substantial behavioral context: account and paid-tier requirements, 15-90s latency, gaps[] never-invented behavior, contradiction detection, hop fields, fetchable citation_uri semantics, semantic excerpting, and the explicit 'NOT open-web search' boundary. This far exceeds what annotations alone convey and does not contradict 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?

Although long, the description is dense and every sentence carries operational significance: auth prerequisites, primary use case, alternative routing, internal behavior, output contents, latency, and citation guarantees. It is front-loaded with the account requirement and the critical distinction from ask_pipeworx, so the most decision-relevant information appears first.

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 explains the return packet: verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[], contradictions[], and hop field. It also covers prerequisites, expected latency, data-source scope, and when results will be empty. An agent has everything needed to invoke this tool correctly and set user expectations.

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 input schema already documents both parameters and the depth enum with 100% coverage. The description still adds value by clarifying that `question` should be broad/multi-part ('decomposition is the point') and by attaching real-world consequences to `depth` choices, such as paid-plan requirement, latency expectations, gap-recovery behavior, and contradiction scans.

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 opens with a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources' in ONE call, and immediately distinguishes itself from open-web search and from the sibling ask_pipeworx. It also explains the internal mechanics (decomposition, parallel routing, findings packet), so an agent knows exactly what this tool is and is not.

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 explicitly states when to use it ('Best for broad/multi-part questions') and when not to: 'For a single lookup use ask_pipeworx' and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'. It also gives concrete example questions and names the alternative tool, leaving no ambiguity about selection.

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