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 mentions the return type ('Returns list of n-gram lists'), which is helpful, but fails to describe other traits such as error handling, performance characteristics, or what constitutes valid tokens. For a tool with no annotation coverage, this leaves significant 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 appropriately sized with two sentences that are front-loaded and efficient. The first sentence states the purpose, and the second specifies the return value, with no wasted words. It could be slightly improved by integrating the return info into the first sentence for better flow.

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 (2 parameters, no annotations, but with an output schema), the description is minimally adequate. The output schema likely covers return values, reducing the need for detailed output explanation. However, it lacks context on usage scenarios and behavioral details, making it incomplete for optimal agent guidance.

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 no parameter semantics beyond what the input schema provides, as schema description coverage is 0%. However, the schema itself is simple with only two parameters (tokens and n), and the tool name implies their usage. The baseline is 3 since the schema does the heavy lifting with clear titles and types, though the description doesn't compensate for the coverage gap.

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 ('n-grams from a list of tokens'), making the purpose specific and understandable. It distinguishes from siblings like 'generate_char_ngrams' by specifying token-based n-grams, though it doesn't explicitly contrast with all alternatives.

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 like 'generate_char_ngrams' or other text processing tools in the sibling list. It lacks context about typical use cases or prerequisites, leaving the agent to infer usage from the name alone.

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