score_data

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

With no annotations, the description must disclose behavioral traits like side effects, permissions, or output. It only states the high-level purpose, leaving the agent unaware of whether scoring modifies data or requires special access.

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 a single, concise sentence that front-loads the core action and resource. There is no redundant phrasing, though the brevity omits necessary operational context.

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?

The tool has nested objects and an output schema, but the description does not explain what the score output contains, how input_data should be structured, or how errors are handled. This leaves significant operational ambiguity for an agent.

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 descriptions already cover all three parameters with clear names and purposes. The description adds the context of scoring against a published model, which aligns with the schema, but it does not provide additional parameter-level detail beyond what is already in the schema.

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 identifies the action ('Score') and the target ('data against a published model or decision'), with a domain hint ('MAS module'). This distinguishes it from sibling tools like list_models_and_decisions or run_ml_project.

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?

No guidance is provided on when to use this tool versus alternatives. It does not state prerequisites, expected workflows, or scenarios where a different tool (e.g., run_ml_project) would be more appropriate.

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