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

Annotations already establish readOnly/openWorld/idempotent/non-destructive, and the description adds substantial behavioral detail: extraction constrained to tool results, explicit refusal modes with refusal_reason enum values, and a note about an extra LLM call. No contradiction with 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?

Front-loaded with the core purpose, then mechanism, return shape, refusal semantics, usage guidance, and cost tradeoff. Every sentence adds actionable information; despite length, nothing is redundant.

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?

For a complex tool with no output schema, the description fully documents success and refusal return shapes, lists all refusal reasons, clarifies routing behavior, and gives usage criteria. An agent has enough to select and invoke it 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 coverage is 100% and describes all six params as aliases for a natural-language question. The description adds no new parameter-level meaning, but the schema already carries the full burden, so baseline 3 is appropriate.

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?

Description states a specific mode: 'Hallucination-resistant answer mode for high-stakes reads' and contrasts it with ask_pipeworx, the casual alternative. It clearly conveys the answer extraction behavior and distinguishes itself from sibling tools in name and function.

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' with examples. It also gives an alternative and a preference rule: 'prefer ask_pipeworx for casual lookups', plus a cost tradeoff note.

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