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

Annotations already provide readOnly/openWorld/idempotent/non-destructive hints, but the description adds substantial behavioral context: auth and paid-tier requirements, parallel routing, verbatim evidence with confidence/source/fetched_at, gaps[] never-invented behavior, contradictions[] for standard/thorough, semantic excerpting, and latency expectations. This goes well beyond what the structured annotations express.

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 the tool is complex and has no output schema, so the length is largely earned. It is front-loaded with the most critical operational facts: account requirement, the alternative to use when unsigned-in, and the NOT open-web-search caveat. It could be tightened slightly, but it is dense with distinct facts rather than padded.

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 fully carries the burden of explaining return behavior, and it does: findings packet, verbatim evidence, confidence, source, fetched_at, citation_uri, hop, gaps[], contradictions[], and excerpting. Combined with rich parameter descriptions and annotations, an agent has everything needed to select and invoke this tool 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 schema already documents both parameters at 100% coverage, so the description is not required to compensate. It adds meaningful context by tying depth levels to hop behavior, paid plans, latency, and contradiction scans, and by giving example question forms. This exceeds the baseline but does not provide field-level syntax 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 action ('research'), a specific resource ('Pipeworx's 1497 STRUCTURED data sources'), and clearly distinguishes itself from open-web search and from ask_pipeworx. It moves beyond tautology by explaining what makes this tool unique: parallel decomposition across many tools into a findings packet.

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: broad/multi-part structured-data questions are the intended use, single lookups should use ask_pipeworx, breaking/current-news queries should prefer ask_pipeworx, and unsigned-in users are routed to ask_pipeworx. This is model-friendly selection criteria, not just a vague hint.

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