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

Annotations already establish read-only, idempotent, open-world behavior. The description adds substantial behavioral context: parallel routing across 5,798 tools, verbatim evidence with gaps[], never inventing answers, always-fetchable citation_uri, contradictions[] for standard/thorough, semantic excerpting of large records, and latency expectations. No contradiction with 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 runs as a single dense paragraph, but nearly every sentence carries operational value: account prerequisite, fallback tool, capability, output structure, depth behavior, citation guarantees, and latency. It is front-loaded with the account constraint and alternative, though depth behavior is somewhat redundantly repeated from the schema.

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 must explain return values and it does thoroughly: findings packet with verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], and hop. It also covers prerequisites, latency, depth variants, and gives concrete example questions. For a complex research tool, nothing critical is missing.

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 description coverage is 100%, so the schema already documents both parameters including depth tiers, hop behavior, and the paid requirement. The tool description adds minor extra semantics like latency for thorough and reinforces the paid plan, but mostly repeats what the schema already states. This matches the high-coverage baseline.

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 verb and resource: grounded multi-source research across Pipeworx's 1,517 structured data sources, and explicitly contrasts itself with open-web search. It also describes the decomposition/facets mechanism and the findings packet output, making the tool's role and distinct capabilities clear relative to siblings like ask_pipeworx.

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 says to use ask_pipeworx when not signed in and for single lookups, while positioning deep_research as best for broad/multi-part questions over structured data. This gives concrete when-to-use and when-not-to-use guidance with a named alternative, leaving little to inference.

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