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Context Protocol integration for Claude Context","maintainers":[{"name":"dannyboy2042","email":"danielbowne@gmail.com"}],"readme":"# @zilliz/claude-context-mcp\n![](../../assets/claude-context.png)\nModel Context Protocol (MCP) integration for Claude Context - A powerful MCP server that enables AI assistants and agents to index and search codebases using semantic search.\n\n[![npm version](https://img.shields.io/npm/v/@zilliz/claude-context-mcp.svg)](https://www.npmjs.com/package/@zilliz/claude-context-mcp)\n[![npm downloads](https://img.shields.io/npm/dm/@zilliz/claude-context-mcp.svg)](https://www.npmjs.com/package/@zilliz/claude-context-mcp)\n\n> 📖 **New to Claude Context?** Check out the [main project README](../../README.md) for an overview and setup instructions.\n\n\n## 🚀 Use Claude Context as MCP in Claude Code and others\n\n![img](https://lh7-rt.googleusercontent.com/docsz/AD_4nXf2uIf2c5zowp-iOMOqsefHbY_EwNGiutkxtNXcZVJ8RI6SN9DsCcsc3amXIhOZx9VcKFJQLSAqM-2pjU9zoGs1r8GCTUL3JIsLpLUGAm1VQd5F2o5vpEajx2qrc77iXhBu1zWj?key=qYdFquJrLcfXCUndY-YRBQ)\n\nModel Context Protocol (MCP) allows you to integrate Claude Context with your favorite AI coding assistants, e.g. Claude Code.\n\n\n## Quick Start\n\n### Prerequisites\n\nBefore using the MCP server, make sure you have:\n- API key for your chosen embedding provider (OpenAI, VoyageAI, Gemini, or Ollama setup)\n- Milvus vector database (local or cloud)\n\n> 💡 **Setup Help:** See the [main project setup guide](../../README.md#-quick-start) for detailed installation instructions.\n\n### Prepare Environment Variables\n\n#### Embedding Provider Configuration\n\nClaude Context MCP supports multiple embedding providers. Choose the one that best fits your needs:\n\n> 💡 **Tip**: You can also use [global environment variables](../../docs/getting-started/environment-variables.md) for easier configuration management across different MCP clients.\n\n```bash\n# Supported providers: OpenAI, VoyageAI, Gemini, Ollama\nEMBEDDING_PROVIDER=OpenAI\n```\n\n<details>\n<summary><strong>1. OpenAI Configuration (Default)</strong></summary>\n\nOpenAI provides high-quality embeddings with excellent performance for code understanding.\n\n```bash\n# Required: Your OpenAI API key\nOPENAI_API_KEY=sk-your-openai-api-key\n\n# Optional: Specify embedding model (default: text-embedding-3-small)\nEMBEDDING_MODEL=text-embedding-3-small\n\n# Optional: Custom API base URL (for Azure OpenAI or other compatible services)\nOPENAI_BASE_URL=https://api.openai.com/v1\n```\n\n**Available Models:**\n- `text-embedding-3-small` (1536 dimensions, faster, lower cost)\n- `text-embedding-3-large` (3072 dimensions, higher quality)\n- `text-embedding-ada-002` (1536 dimensions, legacy model)\n\n**Getting API Key:**\n1. Visit [OpenAI Platform](https://platform.openai.com/api-keys)\n2. Sign in or create an account\n3. Generate a new API key\n4. Set up billing if needed\n\n</details>\n\n<details>\n<summary><strong>2. VoyageAI Configuration</strong></summary>\n\nVoyageAI offers specialized code embeddings optimized for programming languages.\n\n```bash\n# Required: Your VoyageAI API key\nVOYAGEAI_API_KEY=pa-your-voyageai-api-key\n\n# Optional: Specify embedding model (default: voyage-code-3)\nEMBEDDING_MODEL=voyage-code-3\n```\n\n**Available Models:**\n- `voyage-code-3` (1024 dimensions, optimized for code)\n- `voyage-3` (1024 dimensions, general purpose)\n- `voyage-3-lite` (512 dimensions, faster inference)\n\n**Getting API Key:**\n1. Visit [VoyageAI Console](https://dash.voyageai.com/)\n2. Sign up for an account\n3. Navigate to API Keys section\n4. Create a new API key\n\n</details>\n\n<details>\n<summary><strong>3. Gemini Configuration</strong></summary>\n\nGoogle's Gemini provides competitive embeddings with good multilingual support.\n\n```bash\n# Required: Your Gemini API key\nGEMINI_API_KEY=your-gemini-api-key\n\n# Optional: Specify embedding model (default: gemini-embedding-001)\nEMBEDDING_MODEL=gemini-embedding-001\n```\n\n**Available Models:**\n- `gemini-embedding-001` (3072 dimensions, latest model)\n\n**Getting API Key:**\n1. Visit [Google AI Studio](https://aistudio.google.com/)\n2. Sign in with your Google account\n3. Go to \"Get API key\" section\n4. Create a new API key\n\n</details>\n\n<details>\n<summary><strong>4. Ollama Configuration (Local/Self-hosted)</strong></summary>\n\nOllama allows you to run embeddings locally without sending data to external services.\n\n```bash\n# Required: Specify which Ollama model to use\nEMBEDDING_MODEL=nomic-embed-text\n\n# Optional: Specify Ollama host (default: http://127.0.0.1:11434)\nOLLAMA_HOST=http://127.0.0.1:11434\n```\n\n**Available Models:**\n- `nomic-embed-text` (768 dimensions, recommended for code)\n- `mxbai-embed-large` (1024 dimensions, higher quality)\n- `all-minilm` (384 dimensions, lightweight)\n\n**Setup Instructions:**\n1. Install Ollama from [ollama.ai](https://ollama.ai/)\n2. Pull the embedding model:\n   ```bash\n   ollama pull nomic-embed-text\n   ```\n3. Ensure Ollama is running:\n   ```bash\n   ollama serve\n   ```\n\n</details>\n\n#### Get a free vector database on Zilliz Cloud\n\nClaude Context needs a vector database. You can [sign up](https://cloud.zilliz.com/signup?utm_source=github&utm_medium=referral&utm_campaign=2507-codecontext-readme) on Zilliz Cloud to get an API key.\n\n![](../../assets/signup_and_get_apikey.png)\n\nCopy your Personal Key to replace `your-zilliz-cloud-api-key` in the configuration examples.\n\n```bash\nMILVUS_TOKEN=your-zilliz-cloud-api-key\n``` \n\n\n#### Embedding Batch Size\nYou can set the embedding batch size to optimize the performance of the MCP server, depending on your embedding model throughput. The default value is 100.\n```bash\nEMBEDDING_BATCH_SIZE=512\n```\n\n#### Custom File Processing (Optional)\nYou can configure custom file extensions and ignore patterns globally via environment variables:\n\n```bash\n# Additional file extensions to include beyond defaults\nCUSTOM_EXTENSIONS=.vue,.svelte,.astro,.twig\n\n# Additional ignore patterns to exclude files/directories\nCUSTOM_IGNORE_PATTERNS=temp/**,*.backup,private/**,uploads/**\n```\n\nThese settings work in combination with tool parameters - patterns from both sources will be merged together.\n\n## Usage with MCP Clients\n\n\n<details>\n<summary><strong>Qwen Code</strong></summary>\n\nCreate or edit the `~/.qwen/settings.json` file and add the following configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"@zilliz/claude-context-mcp@latest\"],\n      \"env\": {\n        \"OPENAI_API_KEY\": \"your-openai-api-key\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n</details>\n\n<details>\n<summary><strong>Cursor</strong></summary>\n\nGo to: `Settings` -> `Cursor Settings` -> `MCP` -> `Add new global MCP server`\n\nPasting the following configuration into your Cursor `~/.cursor/mcp.json` file is the recommended approach. You may also install in a specific project by creating `.cursor/mcp.json` in your project folder. See [Cursor MCP docs](https://docs.cursor.com/context/model-context-protocol) for more info.\n\n**OpenAI Configuration (Default):**\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@zilliz/claude-context-mcp@latest\"],\n      \"env\": {\n        \"EMBEDDING_PROVIDER\": \"OpenAI\",\n        \"OPENAI_API_KEY\": \"your-openai-api-key\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n**VoyageAI Configuration:**\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@zilliz/claude-context-mcp@latest\"],\n      \"env\": {\n        \"EMBEDDING_PROVIDER\": \"VoyageAI\",\n        \"VOYAGEAI_API_KEY\": \"your-voyageai-api-key\",\n        \"EMBEDDING_MODEL\": \"voyage-code-3\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n**Gemini Configuration:**\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@zilliz/claude-context-mcp@latest\"],\n      \"env\": {\n        \"EMBEDDING_PROVIDER\": \"Gemini\",\n        \"GEMINI_API_KEY\": \"your-gemini-api-key\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n**Ollama Configuration:**\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@zilliz/claude-context-mcp@latest\"],\n      \"env\": {\n        \"EMBEDDING_PROVIDER\": \"Ollama\",\n        \"EMBEDDING_MODEL\": \"nomic-embed-text\",\n        \"OLLAMA_HOST\": \"http://127.0.0.1:11434\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n</details>\n\n\n\n<details>\n<summary><strong>Void</strong></summary>\n\nGo to: `Settings` -> `MCP` -> `Add MCP Server`\n\nAdd the following configuration to your Void MCP settings:\n\n```json\n{\n  \"mcpServers\": {\n    \"code-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@zilliz/claude-context-mcp@latest\"],\n      \"env\": {\n        \"OPENAI_API_KEY\": \"your-openai-api-key\",\n        \"MILVUS_ADDRESS\": \"your-zilliz-cloud-public-endpoint\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n</details>\n\n<details>\n<summary><strong>Claude Desktop</strong></summary>\n\nAdd to your Claude Desktop configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"@zilliz/claude-context-mcp@latest\"],\n      \"env\": {\n        \"OPENAI_API_KEY\": \"your-openai-api-key\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n</details>\n\n<details>\n<summary><strong>Claude Code</strong></summary>\n\nUse the command line interface to add the Claude Context MCP server:\n\n```bash\n# Add the Claude Context MCP server\nclaude mcp add claude-context -e OPENAI_API_KEY=your-openai-api-key -e MILVUS_TOKEN=your-zilliz-cloud-api-key -- npx @zilliz/claude-context-mcp@latest\n\n```\n\nSee the [Claude Code MCP documentation](https://docs.anthropic.com/en/docs/claude-code/mcp) for more details about MCP server management.\n\n</details>\n\n<details>\n<summary><strong>Windsurf</strong></summary>\n\nWindsurf supports MCP configuration through a JSON file. Add the following configuration to your Windsurf MCP settings:\n\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@zilliz/claude-context-mcp@latest\"],\n      \"env\": {\n        \"OPENAI_API_KEY\": \"your-openai-api-key\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n</details>\n\n<details>\n<summary><strong>VS Code</strong></summary>\n\nThe Claude Context MCP server can be used with VS Code through MCP-compatible extensions. Add the following configuration to your VS Code MCP settings:\n\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@zilliz/claude-context-mcp@latest\"],\n      \"env\": {\n        \"OPENAI_API_KEY\": \"your-openai-api-key\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n</details>\n\n<details>\n<summary><strong>Cherry Studio</strong></summary>\n\nCherry Studio allows for visual MCP server configuration through its settings interface. While it doesn't directly support manual JSON configuration, you can add a new server via the GUI:\n\n1. Navigate to **Settings → MCP Servers → Add Server**.\n2. Fill in the server details:\n   - **Name**: `claude-context`\n   - **Type**: `STDIO`\n   - **Command**: `npx`\n   - **Arguments**: `[\"@zilliz/claude-context-mcp@latest\"]`\n   - **Environment Variables**:\n     - `OPENAI_API_KEY`: `your-openai-api-key`\n     - `MILVUS_TOKEN`: `your-zilliz-cloud-api-key`\n3. Save the configuration to activate the server.\n\n</details>\n\n<details>\n<summary><strong>Cline</strong></summary>\n\nCline uses a JSON configuration file to manage MCP servers. To integrate the provided MCP server configuration:\n\n1. Open Cline and click on the **MCP Servers** icon in the top navigation bar.\n\n2. Select the **Installed** tab, then click **Advanced MCP Settings**.\n\n3. In the `cline_mcp_settings.json` file, add the following configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"@zilliz/claude-context-mcp@latest\"],\n      \"env\": {\n        \"OPENAI_API_KEY\": \"your-openai-api-key\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n4. Save the file.\n\n</details>\n\n<details>\n<summary><strong>Augment</strong></summary>\n\nTo configure Claude Context MCP in Augment Code, you can use either the graphical interface or manual configuration.\n\n#### **A. Using the Augment Code UI**\n\n1. Click the hamburger menu.\n\n2. Select **Settings**.\n\n3. Navigate to the **Tools** section.\n\n4. Click the **+ Add MCP** button.\n\n5. Enter the following command:\n\n   ```\n   npx @zilliz/claude-context-mcp@latest\n   ```\n\n6. Name the MCP: **Claude Context**.\n\n7. Click the **Add** button.\n\n------\n\n#### **B. Manual Configuration**\n\n1. Press Cmd/Ctrl Shift P or go to the hamburger menu in the Augment panel\n2. Select Edit Settings\n3. Under Advanced, click Edit in settings.json\n4. Add the server configuration to the `mcpServers` array in the `augment.advanced` object\n\n```json\n\"augment.advanced\": { \n  \"mcpServers\": [ \n    { \n      \"name\": \"claude-context\", \n      \"command\": \"npx\", \n      \"args\": [\"-y\", \"@zilliz/claude-context-mcp@latest\"] \n    } \n  ] \n}\n```\n\n</details>\n\n<details>\n<summary><strong>Gemini CLI</strong></summary>\n\nGemini CLI requires manual configuration through a JSON file:\n\n1. Create or edit the `~/.gemini/settings.json` file.\n\n2. Add the following configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"@zilliz/claude-context-mcp@latest\"],\n      \"env\": {\n        \"OPENAI_API_KEY\": \"your-openai-api-key\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n3. Save the file and restart Gemini CLI to apply the changes.\n\n</details>\n\n<details>\n<summary><strong>Roo Code</strong></summary>\n\nRoo Code utilizes a JSON configuration file for MCP servers:\n\n1. Open Roo Code and navigate to **Settings → MCP Servers → Edit Global Config**.\n\n2. In the `mcp_settings.json` file, add the following configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"@zilliz/claude-context-mcp@latest\"],\n      \"env\": {\n        \"OPENAI_API_KEY\": \"your-openai-api-key\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n3. Save the file to activate the server.\n\n</details>\n\n<details>\n<summary><strong>Other MCP Clients</strong></summary>\n\nThe server uses stdio transport and follows the standard MCP protocol. It can be integrated with any MCP-compatible client by running:\n\n```bash\nnpx @zilliz/claude-context-mcp@latest\n```\n\n</details>\n\n## Features\n\n- 🔌 MCP Protocol Compliance: Full compatibility with MCP-enabled AI assistants and agents\n- 🔍 Semantic Code Search: Natural language queries to find relevant code snippets\n- 📁 Codebase Indexing: Index entire codebases for fast semantic search\n- 🔄 Auto-Sync: Automatically detects and synchronizes file changes to keep index up-to-date\n- 🧠 AI-Powered: Uses OpenAI embeddings and Milvus vector database\n- ⚡ Real-time: Interactive indexing and searching with progress feedback\n- 🛠️ Tool-based: Exposes three main tools via MCP protocol\n\n## Available Tools\n\n### 1. `index_codebase`\nIndex a codebase directory for semantic search.\n\n**Parameters:**\n- `path` (required): Absolute path to the codebase directory to index\n- `force` (optional): Force re-indexing even if already indexed (default: false)\n- `splitter` (optional): Code splitter to use - 'ast' for syntax-aware splitting with automatic fallback, 'langchain' for character-based splitting (default: \"ast\")\n- `customExtensions` (optional): Additional file extensions to include beyond defaults (e.g., ['.vue', '.svelte', '.astro']). Extensions should include the dot prefix or will be automatically added (default: [])\n- `ignorePatterns` (optional): Additional ignore patterns to exclude specific files/directories beyond defaults (e.g., ['static/**', '*.tmp', 'private/**']) (default: [])\n\n### 2. `search_code`\nSearch the indexed codebase using natural language queries.\n\n**Parameters:**\n- `path` (required): Absolute path to the codebase directory to search in\n- `query` (required): Natural language query to search for in the codebase\n- `limit` (optional): Maximum number of results to return (default: 10, max: 50)\n\n### 3. `clear_index`\nClear the search index for a specific codebase.\n\n**Parameters:**\n- `path` (required): Absolute path to the codebase directory to clear index for\n\n\n## Contributing\n\nThis package is part of the Claude Context monorepo. Please see:\n- [Main Contributing Guide](../../CONTRIBUTING.md) - General contribution guidelines  \n- [MCP Package Contributing](CONTRIBUTING.md) - Specific development guide for this package\n\n## Related Projects\n\n- **[@dannyboy2042/claude-context-core](../core)** - Core indexing engine used by this MCP server\n- **[VSCode Extension](../vscode-extension)** - Alternative VSCode integration\n- [Model Context Protocol](https://modelcontextprotocol.io/) - Official MCP documentation\n\n## License\n\nMIT - See [LICENSE](../../LICENSE) for details ","readmeFilename":"README.md"}