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

The description significantly enriches the annotations: it explains open-world routing across 5,738 tools and 1,499 sources, clarifies that only tool-result content is used, enumerates refusal reasons, and discloses the extra LLM call cost. The read-only, idempotent, non-destructive nature implied by annotations is fully consistent with the described behavior. No contradiction.

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 but every sentence earns its place: mode, routing, extraction rule, return shape, refusal reasons, high-stakes usage context, and cost tradeoff. It is front-loaded with the most critical purpose signal. Despite its length, there is no filler or repetition.

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?

Given the absence of an output schema and the tool's high complexity, the description fully compensates by specifying both success and refusal response shapes, listing refusal reason enums, providing source-count context, and giving cost-based routing guidance. The only omitted details, like latency or rate limits, are not critical for correct invocation.

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 input schema already provides 100% coverage, including aliases for the sole required parameter. The description adds no parameter-specific semantics beyond the schema, which is acceptable per baseline. It does not confuse parameter semantics with tool behavior, so no penalty, but also no added clarification.

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 clearly identifies this as a grounded, hallucination-resistant answer mode and distinguishes it from ask_pipeworx via the key differentiator: it extracts the answer only from the tool result and refuses when data is insufficient. The return/refusal shapes make the tool's function concrete. This is a specific verb+resource definition with strong sibling differentiation.

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 states when to use: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts...' It also gives the exclusion: 'prefer ask_pipeworx for casual lookups,' with the cost rationale ('Costs one extra LLM call'). This leaves no ambiguity about alternative selection.

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