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

The description discloses account requirements, paid-tier limitations, latency expectations (15–60s, up to ~90s), the findings packet structure, gap reporting, contradiction scanning, citation behavior, and the fact that records are semantically excerpted. These details go far beyond the annotations, which only state read-only/idempotent behavior, and there is no contradiction with the annotations.

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 it front-loads the most critical operational details: account requirement and the fallback to ask_pipeworx. Every block adds real information: behavior, output format, timing, citations, contradictions, and excerpting. A little trimming and tighter organization could improve readability, but none of the length is filler.

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 when to use it, why to use it over alternatives, auth prerequisites, pricing, return payload structure, citation resolvability, failure/coverage behavior, parameter semantics, and time expectations. An agent has enough context to select and invoke the tool correctly in most realistic scenarios.

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 the baseline is 3, but the description significantly enriches parameter understanding. It explains the depth enum behavior in plain language ('quick=3 single hop', 'standard... adds a gap-recovery hop', 'thorough=6 paid'), connects depth to pricing, and clarifies that broad multi-part questions are accepted for the question parameter. This adds meaningful decision-making value 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 names a specific verb and resource: decomposing a research question and grounding answers across Pipeworx's 1517 structured data sources. It clearly distinguishes itself from open-web search and from ask_pipeworx, mentioning the sibling explicitly. The one-call parallel research model is concrete enough for an agent to know exactly what this tool does.

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 gives explicit when-to-use guidance: 'Best for broad/multi-part questions over structured data' and 'For a single lookup use ask_pipeworx instead.' It also provides a critical exclusion: if the user is not signed in, use ask_pipeworx because deep_research requires an account. Alternatives are named and conditions are precise.

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