Semantic Density Scorer Connector for AI agents.

It audits your capability definitions to ensure they are precise and structurally sound. This prevents errors during the execution phase.

Yes, it specifically checks for naming inconsistencies like mixed casing styles that can break your integration.

It works with any MCP-compatible client, including Cursor, VS Code, and Claude Desktop.

Yes, by identifying unnecessary fluff in your descriptions, you can rewrite them to be more dense and efficient.

Absolutely. It provides a way to measure the linguistic precision of your instructions, which is core to effective prompt engineering.

Semantic density refers to the ratio of actionable information to total text length. A high-density description uses imperative verbs and provides clear return types, minimizing linguistic noise that can distract an LLM during function calling.

The analyze_naming_uniformity capability inspects an array of parameter names to detect deviations from a primary casing convention, such as camelCase or snake_case. It returns a uniformity score and identifies the detected style.

Yes. By using evaluate_description_clarity, you can identify descriptions that lack explicit return types or use ambiguous language, allowing you to refine your capabilities for more deterministic and reliable execution in AI clients.

Verb density is the ratio of imperative/action verbs to the total word count in a description. High density indicates more direct instructions for the LLM.

The clarity score is a composite metric that rewards explicit return-type definitions and penalizes descriptions that are either too brief or too verbose.

Yes, you can use calculate_naming_uniformity to verify if parameter names follow either camelCase or snake_case conventions.