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

Beyond the readOnly/idempotent/non-destructive annotations, the description discloses the refusal contract: explicit refusal_reason values ('not_in_source', 'no_tool_match', 'tool_error', 'data_truncated', 'llm_error'), and the guarantee that answers use only the fetched tool result. It also reveals the cost penalty, which is behavioral context an agent would not otherwise know.

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 four sentences are dense but front-loaded: purpose, mechanism, return/refusal contract, usage guidance, and cost trade-off each get exactly one role. No sentence is filler, and the structure moves from what the tool is to how to use it.

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 specifies both success and refusal shapes, including the meaning of refusal_reason values. Combined with the explicit use-case guidance and schema-covered parameters, nothing essential is missing for an agent to invoke and interpret 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?

Schema description coverage is 100%: every parameter is documented as an alias for 'question,' and the question parameter itself lists all accepted aliases. The description adds no additional parameter meaning, so the baseline score of 3 applies; the schema, not the description, carries the parameter documentation burden.

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 opens by defining it as a 'hallucination-resistant answer mode for high-stakes reads' and explains that it routes like ask_pipeworx but extracts answers only from tool results. This clearly distinguishes it from the sibling ask_pipeworx while stating its mechanism and purpose.

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: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' and it names the alternative for other cases: 'prefer ask_pipeworx for casual lookups.' It also quantifies the trade-off (one extra LLM call), so an agent can make the routing decision directly.

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