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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description discloses critical behavior: it extracts answers only from tool results, returns a structured success object with evidence and confidence, and has an explicit refusal mechanism with specific reasons. It also notes the extra LLM call cost. This goes far beyond what annotations reveal.

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 earns its place. It front-loads the core purpose, then details the return format, refusal reasons, use cases, and cost tradeoff. The structure is logical and easy to parse, with no fluff.

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 high complexity (routing across 5,724 tools, multiple refusal modes, and no output schema), the description is fully complete. It explains success and failure return shapes, the extra cost, why to use it, and when not to. An agent has everything needed to invoke it 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% explaining all six parameters (question and aliases). The description does not add extra parameter semantics, but since the schema fully documents them, the baseline 3 is appropriate. No additional meaning is needed.

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 a specific verb (ask) and resource (Pipeworx grounded) with a defined behavior: hallucination-resistant answer mode for high-stakes reads. It explicitly distinguishes itself from the sibling ask_pipeworx by noting the extra LLM call and use case, so an agent can tell them apart without inspecting 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?

It gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' and explicitly prefers ask_pipeworx for casual lookups. This leaves no ambiguity about which tool to choose in different contexts.

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