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

The description goes well beyond the annotations by detailing the exact success return shape ({answer, evidence, confidence, source, fetched_at, refusal_reason:null}) and the explicit refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error). It also discloses the extra LLM call cost, which is material behavioral context not captured elsewhere.

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 serves a purpose: it defines the mode, explains the routing behavior, specifies grounding, enumerates return/refusal shapes, gives use cases, and states cost tradeoff. The key differentiator is front-loaded before the detailed contract.

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?

Despite having no output schema, the description fully covers the return value contract, refusal scenarios, cost implications, and usage context. Combined with the annotations (readOnly, openWorld, idempotent, non-destructive), an agent has complete information to invoke and interpret the 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?

Schema description coverage is 100%: the schema already documents the question parameter and all aliases (q, text, input, query, prompt). The description adds no additional parameter-level semantics, so the baseline of 3 applies.

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 hallucination-resistant answer mode for high-stakes reads, with a specific verb, resource, and distinct behavior: extracting answers using ONLY the tool result. It explicitly differentiates from ask_pipeworx by describing the extra grounded-extraction step, so an agent can distinguish them.

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 states exactly when to use this tool ('whenever an answer will be quoted, cited, or acted on... must not invent facts') and when not to ('prefer ask_pipeworx for casual lookups'). It also notes the cost tradeoff of one extra LLM call, giving clear decision guidance.

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