Code Search MCP Server

High-performance, batch-oriented MCP (Model Context Protocol) code understanding toolkit for AI agents, specially optimized for Java.

Designed to solve the challenge where AI agents get "lost" in large codebases or lack precise context. It focuses on deep parsing, parallel batch processing, and panoramic context to explore large codebases efficiently, significantly reducing token usage while improving logical understanding.

Use Cases

• Panoramic Context: Read multiple files in one call with dependency context auto-expanded.

• Structural Mapping: Build project outlines fast with deep Java annotation awareness.

• Precise Surgery: Precisely locate classes/methods/definitions and return in batch.

Related MCP server: Knowledge Graph MCP Server

Core Tools

Design Notes

• stdio transport: JSON-RPC 2.0 via standard I/O.

• Deterministic protocol: absolute paths only, no path wildcards.

• Java outline enhancement: annotation backtracking merged into signatures.

Java Spring Deep Fit

More than just text search, it understands business logic:

• Layer-Aware Ordering: Intelligent sorting (Controller → Service → Impl → MQ → Repository) so AI understands the business flow immediately.

• Deep Annotation-Awareness: Instead of just seeing signatures, AI sees merged annotations (like @Transactional, @PreAuthorize) to understand business semantics.

• Smart DI Parsing: Automatically identifies injected fields (dependencies), eliminating the need for AI to guess where userService comes from.

• Project Structure: Full support for src/main/java and multi-module Maven/Gradle projects.

Requirements

• Node.js v18.0.0 or later

Install & Build

npm install
npm run build

Integration

"mcpServers": {
  "code-search": {
    "command": "node",
    "args": ["{file}/code-search/index.js"]
  }
}

Tips

• Prefer view_files_full_context for full context in one call.

• Absolute paths are required for stability and reproducibility.

• Complex queries should be split into precise calls.

Open Source

This project is open-sourced under the MIT License. You may use, modify, and distribute it under the license terms.