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

Beyond the readOnly/idempotent annotations, the description discloses concrete refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error), the exact success return shape with verbatim evidence, and the extra LLM call cost. No contradiction with annotations exists.

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-structured: purpose first, then behavior/return shape, then refusal mode, then usage guidance, then cost tradeoff. Every sentence adds decision-relevant information, and the structure is easy to parse.

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?

Given no output schema, the description fully explains success and refusal return structures, routing scope, evidence semantics, and when to choose the sibling. An agent has enough context to invoke it correctly and interpret results.

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?

The input schema covers all parameters at 100%, including canonical parameter `question` and its aliases. The description does not add new parameter-specific semantics; it focuses on tool behavior. The schema already carries the parameter documentation 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?

The description precisely defines what the tool does: it routes a question through Pipeworx, fetches data, and returns an answer extracted strictly from the tool result. It also differentiates itself from ask_pipeworx by naming it and emphasizing the hallucination-resistant, grounded extraction behavior.

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 usage criteria are provided: use it when answers will be quoted, cited, or acted on, and when the agent must not invent facts. It also names ask_pipeworx as the preferred alternative for casual lookups, plus the cost tradeoff of one extra LLM call.

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