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 explains the output scale (0-1) and endpoint meanings, which is helpful, but doesn't describe algorithm specifics (e.g., Levenshtein distance), normalization method, handling of edge cases, or performance characteristics. For a computational 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 extremely concise (one sentence) with zero wasted words. It front-loads the core purpose and immediately explains the output scale, making every word earn its place. This is model efficiency for a straightforward computational tool.

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 computational nature, 2 simple parameters, and existence of an output schema (which presumably documents the return value), the description provides adequate but minimal context. It explains the output scale well but lacks parameter guidance, algorithm details, and sibling tool differentiation that would make it more complete for an AI agent.

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 schema provides no parameter documentation. The description doesn't mention parameters at all, failing to explain what 's1' and 's2' represent or provide usage examples. However, with only 2 simple string parameters and the tool name being self-explanatory, the baseline is 3 as the agent can reasonably infer parameter meanings from 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 normalized edit distance on a 0-1 scale, with specific meaning for the endpoints (0=identical, 1=completely different). This is a clear verb+resource statement, though it doesn't explicitly differentiate from sibling tools like 'get_edit_distance' which likely provides unnormalized distance.

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 about when to use this tool versus alternatives. The description doesn't mention sibling tools like 'get_edit_distance' or 'get_cosine_similarity' for comparison, nor does it suggest appropriate use cases for normalized versus unnormalized metrics.

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