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 for behavioral disclosure. It states what the tool calculates but doesn't describe implementation details (case sensitivity, Unicode handling, performance characteristics), error conditions, or output format. While the concept of edit distance is standard, the description doesn't provide enough operational context for an agent to use it confidently.

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 with two clear sentences that directly explain the tool's purpose and parameters. Every word earns its place with zero redundancy or unnecessary elaboration. The information is front-loaded with the core concept immediately stated.

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 (string comparison algorithm), no annotations, and the presence of an output schema, the description is minimally adequate. The output schema will presumably document return values, reducing the burden on the description. However, for a computational tool with implementation nuances, more behavioral context would be helpful despite the output schema.

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%, so the description must compensate. It mentions both parameters (s1, s2) and explains they're strings to transform, which adds meaning beyond the bare schema. However, it doesn't provide details about parameter constraints, encoding requirements, or examples of valid inputs. The baseline would be lower without this minimal parameter context.

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 'Levenshtein edit distance' with specific parameters (s1, s2), which is a well-defined computational linguistics concept. It distinguishes from siblings by focusing on character-level string comparison rather than cleaning, counting, or other text analysis functions. However, it doesn't explicitly contrast with 'get_normalized_edit_distance' which appears to be a related sibling tool.

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 doesn't mention when edit distance is appropriate versus other similarity metrics like cosine similarity or Jaccard similarity (both available as sibling tools), nor does it explain the relationship with 'get_normalized_edit_distance' which appears to be a normalized version of this calculation.

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