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 classification action and output categories ('positive', 'negative', 'neutral'), but doesn't describe how the classification works (e.g., model used, confidence thresholds), error handling, rate limits, or performance characteristics. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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, efficient sentence that front-loads the core functionality ('Classify text') and specifies the output categories. There is zero waste—every word contributes directly to understanding the tool's purpose and output format.

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 (single input, categorical output), no annotations, and the presence of an output schema (which likely defines the label categories), the description is reasonably complete. It covers the basic operation and output, though it lacks details on behavioral aspects like error handling or model specifics, which would be beneficial given the absence of annotations.

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 description adds meaningful context for the single parameter 'text' by specifying it's the input to be classified, which goes beyond the schema's minimal title ('Text') and 0% coverage. Since there's only one parameter and the schema provides no description, the tool's purpose inherently clarifies the parameter's role, compensating adequately for the low schema coverage.

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: 'Classify text as 'positive', 'negative', or 'neutral''. It specifies the verb (classify) and resource (text), and distinguishes it from siblings like 'get_sentiment_score' by focusing on categorical labels rather than numerical scores. However, it doesn't explicitly differentiate from 'get_aspect_sentiment' or 'get_sentence_sentiments', which are also sentiment-related tools.

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 when to choose this over 'get_sentiment_score' (for numerical sentiment) or 'get_aspect_sentiment' (for aspect-based analysis), nor does it specify any prerequisites or exclusions. Usage is implied by the classification task but lacks explicit context.

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