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

Annotations already mark it read-only, idempotent, open-world, and non-destructive, and the description goes well beyond them. It discloses account requirements, latency expectations, gap[] handling, never-invented findings, contradiction scans, hop fields, and resolvable citation URIs. This gives an unusually complete picture of what the tool will actually do.

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 nearly every sentence carries decision-relevant information: account prerequisites, alternative tool routing, scope limitations, return structure, depth semantics, latency, and citation guarantees. It is front-loaded with the most critical gate (account required) and the fallback tool. A small amount of redundancy with the schema's depth explanation prevents a perfect score, but it is still disciplined for this complex tool.

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 must explain return values, and it does: findings packet contents, confidence, source, fetched_at, pipeworx:// citations, gaps[], contradictions[], hop field, and citation_uri. It also covers failure/limitation behavior and expected latency. An agent has everything needed to decide whether to call it and how to interpret the response.

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%, but the description adds meaningful operational context: depth tiers are tied to paid plans, defaults, hop behavior, and latency differences. It also gives concrete example questions that illustrate how 'question' should be phrased. This adds practical value beyond the schema's field descriptions.

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?

States a specific verb and resource: grounded multi-source research across Pipeworx's structured data sources, not open-web search. It clearly distinguishes itself from ask_pipeworx by describing its decomposition, parallel routing, and findings-packet behavior. The examples of broad/multi-part questions make the intended use unmistakable.

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?

Explicitly says to use ask_pipeworx instead when not signed in and for single lookups, while positioning deep_research as best for broad/multi-part questions over structured data. It also clarifies that 'thorough' requires a paid plan, giving agents an actionable selection criterion. This is strong when/when-not guidance relative to the sibling set.

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