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AutoDev Codebase MCP Server

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@autodev/codebase

A platform-agnostic code analysis library with semantic search capabilities and MCP (Model Context Protocol) server support. This library provides intelligent code indexing, vector-based semantic search, and can be integrated into various development tools and IDEs.

🚀 Features

  • Semantic Code Search: Vector-based code search using embeddings

  • MCP Server Support: HTTP-based MCP server for IDE integration

  • Terminal UI: Interactive CLI with rich terminal interface

  • Tree-sitter Parsing: Advanced code parsing and analysis

  • Vector Storage: Qdrant vector database integration

  • Flexible Embedding: Support for various embedding models via Ollama

📦 Installation

1. Install and Start Ollama

# Install Ollama (macOS) brew install ollama # Start Ollama service ollama serve # In a new terminal, pull the embedding model ollama pull dengcao/Qwen3-Embedding-0.6B:Q8_0

2. Install ripgrep

ripgrep is required for fast codebase indexing. Install it with:

# Install ripgrep (macOS) brew install ripgrep # Or on Ubuntu/Debian sudo apt-get install ripgrep # Or on Arch Linux sudo pacman -S ripgrep

3. Install and Start Qdrant

Start Qdrant using Docker:

# Start Qdrant container docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant

Or download and run Qdrant directly:

# Download and run Qdrant wget https://github.com/qdrant/qdrant/releases/latest/download/qdrant-x86_64-unknown-linux-gnu.tar.gz tar -xzf qdrant-x86_64-unknown-linux-gnu.tar.gz ./qdrant

4. Verify Services Are Running

# Check Ollama curl http://localhost:11434/api/tags # Check Qdrant curl http://localhost:6333/collections

5. Install Autodev-codebase

npm install -g @autodev/codebase

Alternatively, you can install it locally:

git clone https://github.com/anrgct/autodev-codebase cd autodev-codebase npm install npm run build npm link

🛠️ Usage

Command Line Interface

The CLI provides two main modes:

1. Interactive TUI Mode (Default)

# Basic usage: index your current folder as the codebase. # Be cautious when running this command if you have a large number of files. codebase # With custom options codebase --demo # Create a local demo directory and test the indexing service, recommend for setup codebase --path=/my/project codebase --path=/my/project --log-level=info

2. MCP Server Mode (Recommended for IDE Integration)

# Start long-running MCP server cd /my/project codebase mcp-server # With custom configuration codebase mcp-server --port=3001 --host=localhost codebase mcp-server --path=/workspace --port=3002

⚙️ Configuration

Configuration Files & Priority

The library uses a layered configuration system, allowing you to customize settings at different levels. The priority order (highest to lowest) is:

  1. CLI Parameters (e.g., --model, --ollama-url, --qdrant-url, --config, etc.)

  2. Project Config File (./autodev-config.json)

  3. Global Config File (~/.autodev-cache/autodev-config.json)

  4. Built-in Defaults

Settings specified at a higher level override those at lower levels. This lets you tailor the behavior for your environment or project as needed.

Config file locations:

  • Global: ~/.autodev-cache/autodev-config.json

  • Project: ./autodev-config.json

  • CLI: Pass parameters directly when running commands

Global Configuration

Create a global configuration file at ~/.autodev-cache/autodev-config.json:

{ "isEnabled": true, "embedder": { "provider": "ollama", "model": "dengcao/Qwen3-Embedding-0.6B:Q8_0", "dimension": 1024, "baseUrl": "http://localhost:11434" }, "qdrantUrl": "http://localhost:6333", "qdrantApiKey": "your-api-key-if-needed", "searchMinScore": 0.4 }

Project Configuration

Create a project-specific configuration file at ./autodev-config.json:

{ "embedder": { "provider": "openai-compatible", "apiKey": "sk-xxxxx", "baseUrl": "http://localhost:2302/v1", "model": "openai/text-embedding-3-smallnpm", "dimension": 1536, }, "qdrantUrl": "http://localhost:6334" }

Configuration Options

Option

Type

Description

Default

isEnabled

boolean

Enable/disable code indexing feature

true

embedder.provider

string

Embedding provider (

ollama

,

openai

,

openai-compatible

)

ollama

embedder.model

string

Embedding model name

dengcao/Qwen3-Embedding-0.6B:Q8_0

embedder.dimension

number

Vector dimension size

1024

embedder.baseUrl

string

Provider API base URL

http://localhost:11434

embedder.apiKey

string

API key (for OpenAI/compatible providers)

-

qdrantUrl

string

Qdrant vector database URL

http://localhost:6333

qdrantApiKey

string

Qdrant API key (if authentication enabled)

-

searchMinScore

number

Minimum similarity score for search results

0.4

Note: The isConfigured field is automatically calculated based on the completeness of your configuration and should not be set manually. The system will determine if the configuration is valid based on the required fields for your chosen provider.

Configuration Priority Examples

# Use global config defaults codebase # Override model via CLI (highest priority) codebase --model="custom-model" # Use project config with CLI overrides codebase --config=./my-config.json --qdrant-url=http://remote:6333

🔧 CLI Options

Global Options

  • --path=<path> - Workspace path (default: current directory)

  • --demo - Create demo files in workspace

  • --force - ignore cache force re-index

  • --ollama-url=<url> - Ollama API URL (default: http://localhost:11434)

  • --qdrant-url=<url> - Qdrant vector DB URL (default: http://localhost:6333)

  • --model=<model> - Embedding model (default: nomic-embed-text)

  • --config=<path> - Config file path

  • --storage=<path> - Storage directory path

  • --cache=<path> - Cache directory path

  • --log-level=<level> - Log level: error|warn|info|debug (default: error)

  • --log-level=<level> - Log level: error|warn|info|debug (default: error)

  • --help, -h - Show help

MCP Server Options

  • --port=<port> - HTTP server port (default: 3001)

  • --host=<host> - HTTP server host (default: localhost)

IDE Integration (Cursor/Claude)

Configure your IDE to connect to the MCP server:

{ "mcpServers": { "codebase": { "url": "http://localhost:3001/sse" } } }

For clients that do not support SSE MCP, you can use the following configuration:

{ "mcpServers": { "codebase": { "command": "codebase", "args": [ "stdio-adapter", "--server-url=http://localhost:3001/sse" ] } } }

🌐 MCP Server Features

Web Interface

  • Home Page: http://localhost:3001 - Server status and configuration

  • Health Check: http://localhost:3001/health - JSON status endpoint

  • MCP Endpoint: http://localhost:3001/sse - SSE/HTTP MCP protocol endpoint

Available MCP Tools

  • search_codebase - Semantic search through your codebase

    • Parameters: query (string), limit (number), filters (object)

    • Returns: Formatted search results with file paths, scores, and code blocks

Scripts

# Development mode with demo files npm run dev # Build for production npm run build # Type checking npm run type-check # Run TUI demo npm run demo-tui # Start MCP server demo npm run mcp-server

Embedding Models PK

Mainstream Embedding Models Performance

Model

Dimension

Avg Precision@3

Avg Precision@5

Good Queries (≥66.7%)

Failed Queries (0%)

siliconflow/Qwen/Qwen3-Embedding-8B

4096

76.7%

66.0%

5/10

0/10

siliconflow/Qwen/Qwen3-Embedding-4B

2560

73.3%

54.0%

5/10

1/10

voyage/voyage-code-3

1024

73.3%

52.0%

6/10

1/10

siliconflow/Qwen/Qwen3-Embedding-0.6B

1024

63.3%

42.0%

4/10

1/10

morph-embedding-v2

1536

56.7%

44.0%

3/10

1/10

openai/text-embedding-ada-002

1536

53.3%

38.0%

2/10

1/10

voyage/voyage-3-large

1024

53.3%

42.0%

3/10

2/10

openai/text-embedding-3-large

3072

46.7%

38.0%

1/10

3/10

voyage/voyage-3.5

1024

43.3%

38.0%

1/10

2/10

voyage/voyage-3.5-lite

1024

36.7%

28.0%

1/10

2/10

openai/text-embedding-3-small

1536

33.3%

28.0%

1/10

4/10

siliconflow/BAAI/bge-large-en-v1.5

1024

30.0%

28.0%

0/10

3/10

siliconflow/Pro/BAAI/bge-m3

1024

26.7%

24.0%

0/10

2/10

ollama/nomic-embed-text

768

16.7%

18.0%

0/10

6/10

siliconflow/netease-youdao/bce-embedding-base_v1

1024

13.3%

16.0%

0/10

6/10

Ollama-based Embedding Models Performance

Model

Dimension

Precision@3

Precision@5

Good Queries (≥66.7%)

Failed Queries (0%)

ollama/dengcao/Qwen3-Embedding-4B:Q4_K_M

2560

66.7%

48.0%

4/10

1/10

ollama/dengcao/Qwen3-Embedding-0.6B:f16

1024

63.3%

44.0%

3/10

0/10

ollama/dengcao/Qwen3-Embedding-0.6B:Q8_0

1024

63.3%

44.0%

3/10

0/10

ollama/dengcao/Qwen3-Embedding-4B:Q8_0

2560

60.0%

48.0%

3/10

1/10

lmstudio/taylor-jones/bge-code-v1-Q8_0-GGUF

1536

60.0%

54.0%

4/10

1/10

ollama/dengcao/Qwen3-Embedding-8B:Q4_K_M

4096

56.7%

42.0%

2/10

2/10

ollama/hf.co/nomic-ai/nomic-embed-code-GGUF:Q4_K_M

3584

53.3%

44.0%

2/10

0/10

ollama/bge-m3:f16

1024

26.7%

24.0%

0/10

2/10

ollama/hf.co/nomic-ai/nomic-embed-text-v2-moe-GGUF:f16

768

26.7%

20.0%

0/10

2/10

ollama/granite-embedding:278m-fp16

768

23.3%

18.0%

0/10

4/10

ollama/unclemusclez/jina-embeddings-v2-base-code:f16

768

23.3%

16.0%

0/10

5/10

lmstudio/awhiteside/CodeRankEmbed-Q8_0-GGUF

768

23.3%

16.0%

0/10

5/10

lmstudio/wsxiaoys/jina-embeddings-v2-base-code-Q8_0-GGUF

768

23.3%

16.0%

0/10

5/10

ollama/dengcao/Dmeta-embedding-zh:F16

768

20.0%

20.0%

0/10

6/10

ollama/znbang/bge:small-en-v1.5-q8_0

384

16.7%

16.0%

0/10

6/10

lmstudio/nomic-ai/nomic-embed-text-v1.5-GGUF@Q4_K_M

768

16.7%

14.0%

0/10

6/10

ollama/nomic-embed-text:f16

768

16.7%

18.0%

0/10

6/10

ollama/snowflake-arctic-embed2:568m:f16

1024

16.7%

18.0%

0/10

5/10

This server cannot be installed

-
security - not tested
-
license - not tested
-
quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

HTTP-based server that provides semantic code search capabilities to IDEs through the Model Context Protocol, allowing efficient codebase exploration without repeated indexing.

  1. 🚀 Features
    1. 📦 Installation
      1. 1. Install and Start Ollama
      2. 2. Install ripgrep
      3. 3. Install and Start Qdrant
      4. 4. Verify Services Are Running
      5. 5. Install Autodev-codebase
    2. 🛠️ Usage
      1. Command Line Interface
    3. ⚙️ Configuration
      1. Configuration Files & Priority
    4. 🔧 CLI Options
      1. Global Options
      2. MCP Server Options
      3. IDE Integration (Cursor/Claude)
    5. 🌐 MCP Server Features
      1. Web Interface
      2. Available MCP Tools
      3. Scripts
    6. Embedding Models PK

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