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. While it lists the types of statistics returned, it doesn't describe how the tool behaves: whether it processes large texts efficiently, what formats it accepts, if there are rate limits, or what the output structure looks like. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational characteristics.

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, consisting of a single sentence that efficiently lists all key statistics without unnecessary words. Every element ('words, sentences, paragraphs, reading time, readability scores, language') earns its place by clarifying the tool's scope, making it easy to scan and understand quickly.

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 complexity of computing multiple text statistics, no annotations, no output schema, and low parameter schema coverage, the description is incomplete. It doesn't explain the return format, error conditions, or how the statistics are calculated (e.g., algorithms for readability scores). For a tool with no structured output documentation, more detail is needed to guide effective use.

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, so the description must compensate. It implies the parameter 'text' is the input for analysis by mentioning 'Comprehensive text stats', but doesn't specify constraints like text length, encoding, or required format. The description adds minimal semantic context beyond the schema's basic type information, meeting the baseline for low 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: it computes comprehensive text statistics including words, sentences, paragraphs, reading time, readability scores, and language. It specifies the verb ('get') and resource ('text statistics') with concrete examples of what statistics are included. However, it doesn't explicitly differentiate from sibling tools like 'count_words' or 'count_sentences' that perform similar but more specific operations.

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 many sibling tools that handle specific text analysis tasks (e.g., 'count_words', 'flesch_reading_ease', 'detect_text_language'), there's no indication of whether this tool aggregates those functions or serves a different purpose. No context, exclusions, or prerequisites are mentioned.

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