blackmount-nlp-mcp

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

No annotations are provided, so the description carries full burden. It states what the tool does but doesn't disclose behavioral traits like whether it's idempotent, what format the output takes (though output schema exists), error conditions, or performance characteristics. For a text processing tool with no annotation coverage, this is inadequate.

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 states the core functionality without unnecessary words. It's appropriately sized for a straightforward tool and gets directly to the point with zero waste.

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 moderate complexity (text processing with parameters), no annotations, and an output schema that presumably describes the return format, the description is minimally complete. It states what the tool does but lacks important context about when to use it versus the sibling 'generate_ngrams' tool and behavioral characteristics.

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 0%, so the schema provides no parameter descriptions. The description mentions 'character-level n-grams' which implies the 'n' parameter controls n-gram size and 'text' is the input, but doesn't explain valid ranges for 'n' or text encoding considerations. It adds minimal semantic value beyond what's obvious from parameter names.

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 verb ('generate') and resource ('character-level n-grams from text'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from its sibling 'generate_ngrams' (which likely generates word-level n-grams), leaving some ambiguity about when to choose one over the other.

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. With a sibling tool named 'generate_ngrams' that likely serves a similar purpose (but probably at word level), the lack of comparative context is a significant gap. No prerequisites or exclusions are mentioned.

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