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 calculation purpose but doesn't describe how words are defined (e.g., handling of punctuation, numbers, or special characters), what happens with empty text, whether it's case-sensitive, or the output format. For a text processing tool with zero 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 with zero wasted words. It's front-loaded with the core purpose and uses clear, straightforward language. Every word earns its place by specifying both the metric (average word length) and the unit (characters).

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 parameter, no annotations, but with an output schema), the description is minimally adequate. The output schema likely handles return values, reducing the need for output explanation. However, for a text analysis tool among many siblings, more context on usage and behavioral details would improve completeness, especially with no 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 implies a text input is needed but doesn't explicitly mention the 'text' parameter. With 0% schema description coverage (the schema has no descriptions for the 'text' parameter), the description adds minimal value by suggesting text processing. However, it doesn't clarify parameter expectations like text format, length limits, or language requirements, keeping it at the baseline.

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 'Average word length in characters' clearly states what the tool does (calculates average word length) and specifies the unit (characters). It distinguishes from siblings like count_words or get_avg_sentence_length by focusing specifically on word length rather than counting or sentence metrics. However, it doesn't explicitly mention the verb 'calculate' or specify it processes input text.

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 for text analysis (e.g., count_words, get_avg_sentence_length, get_text_statistics), there's no indication of whether this is a standalone metric or part of a broader analysis workflow. No 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.