blackmount-nlp-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only explains the mathematical output range (0-1). It doesn't disclose behavioral aspects like how text is tokenized (word-level implies splitting, but details missing), case sensitivity, handling of punctuation/stopwords, performance characteristics, or error conditions.

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?

Extremely concise single sentence that front-loads the core purpose and includes essential numerical context. Every word earns its place with zero wasted text.

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?

For a 2-parameter similarity calculation tool with output schema, the description covers the basic mathematical operation but lacks important context about text preprocessing, tokenization behavior, and comparison to alternatives. The presence of an output schema reduces the need to describe return values, but behavioral aspects remain underspecified.

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?

Schema description coverage is 0%, but the description implicitly clarifies that both parameters are text inputs for comparison. The 'word-level set overlap' context gives meaning beyond the generic 'Text1' and 'Text2' parameter titles. However, it doesn't specify text format requirements or preprocessing 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 calculates Jaccard similarity at the word-level set overlap with a specific numerical range (0-1). It distinguishes this from siblings like cosine similarity and edit distance by specifying the exact metric. However, it doesn't explicitly mention it's for text comparison, though this is implied by the parameter names.

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?

No guidance is provided on when to use this tool versus alternatives like get_cosine_similarity or get_edit_distance. The description explains what the metric measures but doesn't indicate appropriate use cases or comparisons with sibling tools.

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