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

Beyond annotations, the description discloses the exact output contract: a success object with answer/evidence/confidence/source/fetched_at/refusal_reason, and a refusal object with enumerated refusal reasons. It also reveals that the tool consumes an extra LLM call and only extracts from tool output, adding meaningful behavioral context not present in 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 longer than average but front-loaded with the key purpose and then organized into mechanism, return/refusal contract, and usage guidance. It earns most of its length, with only minor redundancy between 'Hallucination-resistant' and 'using ONLY what the tool result contains'.

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?

There is no output schema, but the description fully substitutes by documenting the success and refusal shapes and every refusal_reason. Combined with a well-covered input schema and annotations, nothing an agent needs to decide whether to call the tool, or to understand its response, is left out.

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 baseline is 3. The description says the tool 'fills arguments' for the underlying pick, but adds no detail about the question parameter itself; the schema already documents natural language and aliases.

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 opens by naming a specific purpose—'Hallucination-resistant answer mode for high-stakes reads'—and states the mechanism: route to the correct tool, fetch data, then extract the answer only from the tool result. It also differentiates from ask_pipeworx by framing this as the grounded, refusal-capable variant.

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?

It explicitly says to use this tool whenever the answer 'will be quoted, cited, or acted on' and facts must not be invented, with concrete domains. It also names the alternative, ask_pipeworx, and tells the agent to prefer that for casual lookups because of the extra LLM call, giving clear when/not guidance.

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