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

Annotations already mark this as read-only, idempotent, and non-destructive, but the description goes far beyond that by disclosing output composition (verbatim evidence, confidence, source, fetched_at, citation), the gaps[] behavior with 'never invented', contradictions[], citation resolvability, semantic excerpting, and expected latency per depth. It also exposes the account/payment constraint, giving the agent crucial operational context.

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 dense and front-loads the most operationally critical facts (account requirement, alternative tool, structured-only scope). Every sentence contributes substantive information, but the opening paragraph is a long run-on chain of parentheticals and clauses that could be more scannable. It is appropriately sized for a complex tool, but not as crisp as it could be.

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?

For a research tool with no output schema, the description is exceptionally complete: it covers authentication, alternative routing, depth semantics, output packet structure, citation/gap/contradiction behaviors, excerpting, and latency. An agent has enough context to select and invoke the tool correctly and to set expectations about what results will look like.

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 describes both parameters with 100% coverage, so the baseline is 3. The description adds meaningful semantics beyond that: it explains how 'depth' changes the research behavior (quick/standard/thorough hop counts, gap-recovery, contradictions, paid tier) and clarifies that 'question' can be broad or multi-part. This is more than the schema alone provides, though it does not add format-level syntax for the question.

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 action — 'Grounded multi-source research across Pipeworx's 1506 STRUCTURED data sources' — and explicitly distinguishes itself from open-web search. It also names the intended use case ('broad/multi-part questions over structured data') and contrasts with ask_pipeworx for single lookups, so an agent can tell it apart from at least the most relevant sibling 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 concrete when-to-use versus when-not-to-use guidance: use ask_pipeworx if not signed in, use ask_pipeworx for a single lookup, and use deep_research for broad/multi-part structured-data questions. It also qualifies the account-tier requirement, so an agent can route correctly in different auth contexts.

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