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

Annotations already cover read-only, idempotent, open-world, and non-destructive traits, and the description adds substantial behavioral context beyond that: it discloses the exact success return shape, explicit refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error), 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?

The description is dense but every sentence carries information: mode definition, routing mechanism, output/refusal contract, usage context, and cost trade-off. It is front-loaded with the core differentiator ('Hallucination-resistant answer mode') and remains structured, though it is a single long paragraph.

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 compensates by specifying the return object and refusal reasons. It also covers the trade-off vs ask_pipeworx and the high-stakes usage context. Combined with the annotation hints and 100% schema coverage, an agent has everything needed to decide and invoke 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%, so the input schema already documents all six parameters (e.g., 'question' with aliases). The description does not add parameter-level meaning; it focuses on routing and output behavior. Baseline 3 is appropriate since the schema carries the semantic load.

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 states a specific verb and resource: 'Hallucination-resistant answer mode' that extracts answers only from tool results. It explicitly differentiates itself from ask_pipeworx by naming it and describing the grounded behavior, so an agent can distinguish the two 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?

Provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also gives the exclusion and alternative: 'prefer ask_pipeworx for casual lookups.' This is direct, actionable routing guidance.

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