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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses meaningful behaviors: it never fabricates gaps ('explicit gaps[]'), standard/thorough return contradictions[], large records are semantically excerpted, and expected latency is 15-90s. These details materially inform an agent's expectations about output shape and runtime without contradicting any 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 and dense with parentheticals, but nearly every sentence earns its place: account requirements, fallback tool, source types, parallel decomposition, return fields, gap handling, contradiction scanning, citation resolvability, excerpting, and latency. Some ordering could be tightened, but it is front-loaded with the most decision-critical info (account requirement and alternative).

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?

There is no output schema, so the description shoulders the burden of explaining return values—and it does: findings packet with verbatim evidence, confidence, source, fetched_at, stable pipeworx:// citations, gaps[], and contradictions[]. It also covers latency, semantic excerpting, and depth behavior, making the tool fully understandable for an agent that needs to invoke it 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 input schema already describes both parameters with 100% coverage, so the baseline is 3. The description adds value by explaining the practical effect of depth values (quick=3, standard=3 with gap recovery, thorough=6 paid) and by clarifying that the question parameters supports broad/multi-part natural language. This goes slightly beyond the schema's own text.

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 a specific verb-resource pair ('Grounded multi-source research across Pipeworx's 1499 STRUCTURED data sources') and explicitly differentiates itself from open-web search and from ask_pipeworx. It names the core behavior—decomposing questions, routing to parallel tools, returning evidence packets—so an agent can immediately understand what deep_research 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 when-not-to-use guidance ('For a single lookup use ask_pipeworx instead'). It also names a concrete alternative for signed-out users and clarifies paid-plan requirements, 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.