mcp-adr-analysis-server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool validates content masking but doesn't explain what 'correctly' entails (e.g., checks for patterns, completeness, or errors), whether it's read-only or has side effects, or what the output looks like. For a validation tool with zero annotation coverage, this is insufficient to inform the agent about its behavior.

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, clear sentence with no wasted words. It's front-loaded with the core purpose and avoids unnecessary elaboration, making it highly efficient and easy for an agent to parse.

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?

Given the complexity of validation (which could involve logic, error reporting, or standards), the description is too minimal. No annotations exist to clarify behavior, and there's no output schema to describe return values. The description doesn't compensate for these gaps, leaving the agent with incomplete information about what the tool does beyond its basic purpose.

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?

The input schema has 100% description coverage, with clear parameter names and descriptions ('Original content before masking', 'Content after masking'). The description doesn't add any meaning beyond this, such as explaining how the parameters interact or what validation criteria are used. Given the high schema coverage, 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 the tool's purpose: 'Validate that content masking was applied correctly.' It uses a specific verb ('validate') and identifies the resource ('content masking'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'apply_basic_content_masking' or 'generate_content_masking', which is why it doesn't achieve a perfect score.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing masked content first), compare it to siblings like 'apply_basic_content_masking' or 'validate_rules', or specify scenarios where validation is required. This leaves the agent with minimal context for tool selection.

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