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

Annotations cover only read-only/idempotent safety; the description goes well beyond them by disclosing the cost overhead, the extraction constraint ('ONLY what the tool result contains'), the full success return shape, and the enumerated refusal taxonomy (not_in_source, no_tool_match, tool_error, data_truncated, llm_error). This is exactly the behavioral context an agent needs to handle failure modes.

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?

Front-loads purpose, then routing, then return shape/refusals, then usage criteria, with zero filler. Given the absence of an output schema, the inline return description earns its length.

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 carries the return-value burden: success payload, evidence quote, confidence, source, timestamp, and all refusal reasons. Combined with 100% schema coverage on the 9 parameters, nothing needed to invoke or interpret the tool is missing.

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 coverage is 100%, so the aliases and the question parameter are already documented in the schema. The description adds no argument syntax or formatting detail beyond the schema's own account of prefer_tool, so baseline 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?

States a specific mode ('hallucination-resistant answer mode for high-stakes reads') and distinguishes itself from the sibling ask_pipeworx by naming the shared routing machinery plus the added extraction step. An agent can tell exactly what this tool does differently from ask_pipeworx without opening either 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?

Explicit when-to-use ('whenever an answer will be quoted, cited, or acted on... financial verdicts, legal claims, medical lookups, public statements') and when-not ('prefer ask_pipeworx for casual lookups'), including the tradeoff (one extra LLM call). Nothing is left to inference.

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