Explain a grading rule

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

With no annotations, the description carries the burden for behavioral disclosure. It clearly states the tool 'returns' information, implying a read-only, non-mutating operation. It also details the content of the rationale, which provides realistic expectations. However, it does not explicitly mention error handling, side effects, or authentication requirements, which would be beneficial for full transparency.

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 two sentences, front-loaded with the purpose, and every clause adds value. The first sentence explains what it returns, and the second gives usage context. There is no redundancy or irrelevant detail.

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 is simple (one parameter, no output schema, no annotations). The description compensates for the missing output schema by listing the content of the rationale, which suggests the return structure. It also explains the tool's role in the broader workflow. While it could be more explicit about the exact return format, the description is complete enough for an experienced user to understand and invoke the tool 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%, so the existing parameter documentation is complete. The tool description does not add additional meaning about the parameter beyond what the schema already provides (e.g., format, case-insensitivity, examples). It refers to 'rule ID' generically but does not elaborate further, so the 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 uses a specific verb ('Returns') and identifies a concrete resource ('the full rationale for one grading rule') with explicit content categories ('what it detects, why it degrades agent behaviour, and how to fix it'). It clearly distinguishes itself from siblings by focusing on explaining a single rule rather than listing or grading.

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?

The description explicitly states when to use the tool ('after grade_mcp_server reports a finding you want to understand or dispute') and what not to do ('rather than guessing what a rule ID means'). This provides clear situational guidance and references the sibling tool as the trigger context.

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