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

Annotations already mark this read-only, idempotent, open-world, and non-destructive, and the description does not contradict them. It adds substantial behavioral detail beyond the annotations: account and paid-tier requirements, latency expectations, the gaps[] mechanism with a 'never invented' guarantee, citation fetchability caveats, the contradictions[] scan on certain depths, and semantic excerpting of large records.

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 dense with decision-relevant information, front-loading the account requirement and primary purpose before diving into mechanics. Each sentence earns its place by conveying a distinct behavioral fact; the length is justified by the tool's complexity, though a slightly tighter structure would be even better.

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 carries the full burden of explaining return values, and it does so thoroughly: findings packet fields, gaps[], contradictions[], hop field, citation_uri, and fetchability semantics. It also covers auth, depth variants, latency, and alternatives, making the tool fully callable and interpretable from the description alone.

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 baseline is 3, but the description adds real value by explaining the behavioral implications of the depth parameter (quick/standard/thorough hop counts, gap recovery, contradiction scan, paid requirement) and clarifying that question should be broad/multi-part natural language. This goes beyond what the schema already communicates.

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: it performs grounded multi-source research across 1,517 structured data sources in one call, and explicitly clarifies it is NOT open-web search. It also distinguishes itself from ask_pipeworx by identifying its niche as broad/multi-part structured-data questions, so an agent can tell it apart from siblings without opening schemas.

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 usage rules: use ask_pipeworx if not signed in, use ask_pipeworx for single lookups, and prefer ask_pipeworx for breaking/current news because deep_research returns mostly empty gaps. It also states deep_research is best for broad/multi-part questions, giving clear when-to-use and when-not-to-use guidance with named alternatives.

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