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

Annotations already mark this as read-only, idempotent, and non-destructive, and the description adds substantial behavioral detail on top: it discloses the routed tool selection, the strict extraction-from-result mechanism, the exact success response shape, and the explicit refusal reasons. It also surfaces the cost tradeoff of one extra LLM call. 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 front-loaded with the most important distinction ('hallucination-resistant answer mode for high-stakes reads') and then efficiently covers mechanism, output contract, refusal cases, usage context, and cost tradeoff. Every sentence delivers useful information with no filler.

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 thoroughly explains what the agent will receive: success fields, refusal reasons, and the conditions that trigger refusal. It also provides routing, safety, and cost context. Nothing essential is missing for an agent to decide when and how to invoke this tool.

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%: all six parameters are aliases for the question and are described in the schema. The tool description adds context about natural-language queries and high-stakes use, but it does not add new parameter-level meaning beyond what the schema already provides. 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 clearly identifies a specific verb ('asks'), resource ('Pipeworx'), and mode ('grounded', hallucination-resistant), and explicitly contrasts with the sibling ask_pipeworx. It states the core behavior: route to the right tool, fetch data, extract an answer only from the result. This makes the tool distinguishable from its siblings without needing to inspect 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?

The description gives explicit when-to-use guidance: use whenever an answer will be quoted, cited, or acted on, and when the agent must not invent facts. It also names the alternative (ask_pipeworx), notes the extra LLM call cost, and tells the agent to prefer the cheaper sibling for casual lookups. This is ideal routing guidance.

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