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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds substantial behavioral context: it returns a structured refusal with specific refusal_reason values, explains the extraction is limited to tool result content, and discloses the extra LLM call cost. It also describes the success return shape ({answer, evidence, confidence, source, fetched_at, refusal_reason:null}). This goes well beyond 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?

The description is dense but well-organized: it front-loads the core purpose, then explains the mechanism, return shape, refusal behavior, and usage guidance. Every sentence earns its place, though it is somewhat long. The structure is logical and scannable, with the key differentiator (grounded extraction) stated early.

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 tool with no output schema, the description fully compensates by documenting the success return shape, the refusal shape, and all possible refusal_reason values. It also covers cost, routing behavior, and when to use it. An agent has everything needed to invoke it correctly and interpret the result.

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 schema documents the 'question' parameter plus seven aliases. The description adds context that the question is in natural language and that the tool routes it to the right tool from 6,426 sources, which helps the agent understand what kind of input is expected. However, the description doesn't add much beyond the schema's alias documentation, so a 4 is appropriate rather than 5.

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 clearly states this is a hallucination-resistant answer mode for high-stakes reads, with a specific verb ('extracts the answer using ONLY what the tool result contains') and resource (same routing as ask_pipeworx). It distinguishes itself from ask_pipeworx by emphasizing the grounded extraction and refusal behavior, making it easy for an agent to differentiate.

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 says 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' and lists example domains (financial verdicts, legal claims, medical lookups, public statements). It also names the alternative (ask_pipeworx) and states the cost tradeoff ('Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups'). This is exemplary when-to-use guidance.

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