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

The description adds meaningful behavioral details beyond the sparse annotations: it explains that the request is authorized, classified, and policy-checked before any provider is called, and that blocked prompts never reach the model. It also clarifies tenant/workspace derivation from the API key. These are valuable contextual details not present in the annotations, though it stops short of covering error handling or side effects.

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?

Two sentences, front-loaded with the primary action, and no redundant fluff. Every sentence provides useful information about what the tool does and its governance behavior.

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 an output schema and 100% schema coverage, the description covers the core purpose and a key behavioral guarantee. It omits potential edge-case behaviors like how errors are surfaced or what side effects might occur (e.g., logging, cost), but those are partly mitigated by the available output schema and the clear governance context.

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 coverage is 100% with clear descriptions for all five parameters. The description adds no extra semantics about the parameters themselves—it only mentions tenant/workspace derivation, which is not a parameter. The schema already carries the burden, so a baseline score of 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 clearly states a specific verb ('Run'), resource ('LLM completion'), and context ('through the ThinkNEO AI gateway'). It also distinguishes the tool from siblings by emphasizing governance and policy-checking before provider calls, making its unique purpose explicit.

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 provides clear context: this is the tool for governed LLM completions, with the governance flow explained. However, it does not explicitly mention alternatives or offer when-not-to-use guidance. Since sibling tools include policy checks and routing, a note distinguishing those would be helpful, but the intended usage is still fairly clear.

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