blackmount-nlp-mcp

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 output is a confidence score (0-1), but lacks details on how the score is computed, thresholds for classification, performance characteristics, or error handling. For a tool with no annotation coverage, this is insufficient.

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 extremely concise—a single sentence that directly states the tool's function and output scale. It's front-loaded with no wasted words, making it easy to parse quickly. Every element earns its place.

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 tool's simplicity (1 parameter, output schema exists), the description is minimally adequate. The output schema likely covers return values, reducing the need for detailed description. However, with no annotations and low schema coverage, it could benefit from more behavioral context to be fully complete.

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 1 parameter with 0% description coverage. The description adds context by implying the parameter is 'text' to analyze, but doesn't elaborate on constraints (e.g., length, encoding) or examples. With low schema coverage, it partially compensates but not fully, aligning with the baseline.

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 function: assessing confidence that text is English on a 0-1 scale. It specifies the verb ('confidence that') and resource ('text'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'detect_text_language', which might offer broader language detection.

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 sibling tools like 'detect_text_language' for comparison, nor does it specify scenarios where confidence scoring is preferred over categorical detection. Usage context is implied but not articulated.

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