policy_to_natural_language

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

The description does not disclose any side effects or limitations, but for a straightforward translation tool that appears non-destructive, the information is adequate. No annotations are provided, so the description carries the full burden, but no misleading claims are made.

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, front-loaded sentence with no wasted words. It efficiently conveys the tool's purpose.

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 no output schema or annotations, but given its low complexity as a simple translation task, the description is nearly complete. However, it does not specify the exact output format beyond 'plain English', which is a minor gap.

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%, with both parameters having descriptions in the schema. The tool description does not add any new meaning beyond what the schema already provides, so 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 the verb 'Explain', the resource 'MCP Mastyf AI policy YAML', the output 'plain English', and the audience 'compliance stakeholders'. It effectively distinguishes from the sibling tool 'natural_language_to_policy' which performs the reverse operation.

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?

While not explicitly stating when or when not to use, the name and description imply that this tool is for converting technical policy YAML into human-readable explanations. The presence of the sibling 'natural_language_to_policy' provides context for when to use each, but no explicit alternatives or exclusions are given.

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