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

Annotations already cover read-only, open-world, idempotent, non-destructive properties, so the bar is lower. The description adds substantial context beyond annotations: successful return shape, explicit refusal reasons, grounding-only behavior, and the extra LLM call cost. 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?

Every sentence earns its place: core purpose, routing behavior, return/refusal contract, usage guidance, and cost tradeoff. The description is dense but well-structured and front-loaded with the most important distinguishing feature.

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?

Complete for a question-answering tool of this complexity: the description covers what it does, how it differs from the sibling, when to use it, what it returns on success and failure, and when to prefer the alternative. No important operational gap remains.

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 the description adds no parameter-specific detail beyond what the schema already documents. The schema fully describes the question parameter and its aliases, so the baseline of 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?

States a specific verb and concept ('hallucination-resistant answer mode'), describes the extraction mechanism, and explicitly distinguishes itself from ask_pipeworx by the grounded extraction behavior. The agent can tell exactly what this tool is for and how it differs from its sibling.

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?

Provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts', with concrete examples. It also names the preferred alternative for casual lookups and states the cost tradeoff, making the selection decision fully specified.

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