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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds substantial behavioral detail beyond those: exact success return fields, explicit refusal reasons ('not_in_source', 'no_tool_match', 'tool_error', 'data_truncated', 'llm_error'), and the constraint that extraction uses 'ONLY what the tool result contains'. There is no contradiction with the annotations.

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?

Every sentence earns its place: it front-loads the core purpose, then routing, extraction constraint, output contract, refusal contract, usage guidance, and cost tradeoff. The text is dense but not bloated; lists of refusal reasons and use cases are structured with punctuation that aids scanning.

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 specifies the return shape and failure modes, which is normally the largest gap for agents. It also gives enough context about tool count, source count, and routing behavior to set expectations. No critical operational detail is missing 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?

Schema description coverage is 100% and all 8 parameters are aliases for the question field. The schema already documents the aliases and natural-language requirement, so the description doesn't need to add much. The description implies the question semantics without discussing parameter syntax, matching the baseline for high coverage.

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 with a specific, differentiated purpose: 'Hallucination-resistant answer mode for high-stakes reads' and immediately contrasts with the sibling ask_pipeworx ('Same routing as ask_pipeworx') while clarifying the unique grounded extraction behavior. It names the verb, resource, and output shape, so an agent can distinguish this from siblings like ask_pipeworx, validate_claim, or deep_research 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?

Explicitly states when to use ('Use whenever an answer will be quoted, cited, or acted on... financial verdicts, legal claims, medical lookups, public statements') and when not to ('prefer ask_pipeworx for casual lookups'). It also names the cost tradeoff ('Costs one extra LLM call vs ask_pipeworx'), making the routing decision fully transparent.

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