How does the verification process work?

It iterates through sample projects, triggers a re-index via MCP tools, performs targeted searches for specific symbols, and validates the resulting file summaries.

How does it handle trivial or empty files?

The skill includes a smart fallback mechanism that identifies larger, non-trivial files in the project directory if the initial summary check is ambiguous.

Does this skill require specific directory structures?

Yes, it is designed to work with the sample projects located in the test/sample-projects directory of the code-index-mcp repository.

What languages does this skill verify?

The skill tests a wide range of languages including Python, Go, Java, JavaScript, TypeScript, C#, Kotlin, Objective-C, Zig, and Dart.

Why is the absolute path required for project setting?

The underlying MCP server requires absolute paths to correctly map the filesystem and ensure that indexing and search tools target the correct files.

Multi-Language Indexing Verifier

Name: Multi-Language Indexing Verifier
Author: johnhuang316

byjohnhuang316

•

700

Security & Testing

Verifies the multi-language indexing and search capabilities of the code-index-mcp server across various programming languages.

About

This skill automates the verification process for the code-index-mcp server, ensuring it accurately parses, indexes, and searches across a wide array of programming languages including Python, Java, Go, TypeScript, and more. By sequentially setting project paths, refreshing indexes, and executing advanced searches against known sample projects, it provides a comprehensive audit of the server's cross-language compatibility and symbol extraction reliability, providing a clear pass/fail report for developer peace of mind.

Key Features

Detailed PASS/FAIL reporting for comprehensive language support audits
Validation of symbol extraction for classes, functions, and interfaces
700 GitHub stars
Smart fallback logic for non-trivial file identification
Deep summary analysis to confirm file parsing accuracy
Automated multi-language verification loop for cross-platform indexing

Use Cases

Regression testing language parsers after server configuration changes
Validating a new installation or update of the code-index-mcp server
Confirming symbol extraction reliability across diverse technical stacks

About

Key Features

Detailed PASS/FAIL reporting for comprehensive language support audits
Validation of symbol extraction for classes, functions, and interfaces
700 GitHub stars
Smart fallback logic for non-trivial file identification
Deep summary analysis to confirm file parsing accuracy
Automated multi-language verification loop for cross-platform indexing

Use Cases

Regression testing language parsers after server configuration changes
Validating a new installation or update of the code-index-mcp server
Confirming symbol extraction reliability across diverse technical stacks