blackmount-nlp-mcp

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it returns 'top 5 matches with confidence scores' (output format) and 'Supports 18 languages' (capability scope). This adds valuable context beyond the minimal input schema, though it doesn't cover aspects like performance, rate limits, or error handling.

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 front-loaded with the core purpose, followed by key behavioral details in two concise sentences. Every sentence adds value: the first states the action, the second specifies output format and language support, with zero wasted words.

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 low complexity (1 parameter) and the presence of an output schema (which likely covers return values), the description is reasonably complete. It covers purpose, output behavior, and scope, though it could benefit from more explicit usage guidelines or error case mentions to be fully comprehensive.

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 0% description coverage, so the description must compensate. It adds meaning by specifying that the 'text' parameter is used for language detection, which clarifies the parameter's purpose beyond the schema's basic type definition. However, it doesn't detail constraints like text length or format.

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 ('Detect language from text') with a specific verb and resource. It distinguishes from siblings by focusing on language detection rather than text cleaning, analysis, or other operations. However, it doesn't explicitly differentiate from potential similar tools not in the sibling list.

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 implies usage context through 'Supports 18 languages,' suggesting when this tool is applicable. However, it provides no explicit guidance on when to use this versus alternatives like 'check_is_english' or other language-related tools, nor does it mention exclusions or prerequisites.

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