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 mentions 'average sentence length in words', implying a calculation, but doesn't disclose behavioral traits like how sentences are defined (e.g., punctuation-based), error handling for empty text, or output format. This is a significant gap for a tool with no annotation coverage.

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 at four words, front-loaded with the core purpose. There's zero waste or redundancy, making it easy to parse quickly. It efficiently communicates the essential function without unnecessary detail.

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 (one parameter) and the presence of an output schema, the description is minimally complete. However, with no annotations and many sibling tools, it lacks context on behavior and usage. The output schema likely covers return values, but the description doesn't add enough value beyond structured data for full completeness.

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 the input schema, which has 0% description coverage for the single 'text' parameter. However, with only one parameter and a clear tool name, the baseline is 3 as the schema alone might suffice minimally. The description doesn't compensate for the coverage gap but doesn't worsen it either.

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 sentence length in words' clearly states what the tool calculates, but it's somewhat vague about the exact verb and scope. It doesn't explicitly distinguish from siblings like 'count_words' or 'get_avg_word_length', though the name 'get_avg_sentence_length' helps. The description is functional but lacks specificity about how it processes 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_sentences', 'get_avg_word_length'), there's no indication of context, prerequisites, or comparisons. This leaves the agent to infer usage based on the name alone.

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