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 handling specific text elements (contractions, hyphenated words, numbers, punctuation), which adds some context about tokenization behavior, but lacks details on output format, error handling, performance, or language-specific considerations, making it insufficient for a mutation-like tool.

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 and front-loaded: a single sentence that directly states the tool's function and key capabilities. Every word earns its place, with no wasted text, making it highly efficient and easy to parse for an AI agent.

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 specific handling rules), no annotations, and the presence of an output schema (which covers return values), the description is minimally adequate. It explains the core function but lacks details on behavioral traits and usage guidelines, leaving gaps that the output schema alone doesn't fill.

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, and the description adds meaningful context by implying the 'text' parameter should contain the input to be tokenized. Since there are 0 parameters with schema descriptions, the baseline is 4, and the description compensates adequately by clarifying the parameter's role without redundant details.

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: 'Split text into word tokens' with specific verb ('split') and resource ('text'), and it distinguishes itself from siblings like 'sentence_tokenize' by focusing on word-level tokenization. However, it doesn't explicitly differentiate from other text processing tools like 'clean_remove_punctuation' or 'porter_stem', which keeps it from a perfect score.

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 mentions handling of contractions, hyphenated words, numbers, and punctuation, but doesn't specify scenarios or exclusions, such as when to prefer 'clean_remove_punctuation' for punctuation removal or 'porter_stem' for stemming, leaving the agent without usage context.

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