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

Even though annotations already declare read-only, open-world, and idempotent behavior, the description adds substantial context beyond those hints: what the findings packet contains, how gaps[] and contradictions[] behave, the latency profile, that citations are only returned when resolvable, and that it never invents answers. This goes well beyond the annotation surface and helps an agent set expectations correctly.

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 it is well-structured and front-loaded with the most decision-critical information: account requirement, alternative tool, and what the tool is not. Every substantial block adds a distinct behavioral fact, though a slightly tighter organization would reduce cognitive load for an agent scanning for invocation rules.

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 correctly carries the burden of explaining return values: findings, evidence quotes, confidence, source, fetched_at, citation_uri, gaps[], and contradictions[]. It also covers prerequisites, latency, depth semantics, and alternative routing, making it complete for a complex tool with only two parameters.

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%, and the input schema already explains both `question` and the depth enum in detail. The description reinforces depth behavior (gaps, contradictions, latency) but largely restates what the schema already captures, with only marginal extra context like the explicit quick/standard/thorough distinction and paid-plan constraint.

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 over Pipeworx's structured data sources in one call. It explicitly contrasts itself with open-web search and names the key sibling alternative (ask_pipeworx), so an agent can tell them apart without inspecting either tool's schema.

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?

It gives explicit when-to-use guidance: best for broad/multi-part questions over structured data, and explicitly says to use ask_pipeworx instead for single lookups or when not signed in. It also explains the depth-level selection and the account/plan gate, 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.