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Model Context Protocol integration for Claude Context with external project management","maintainers":[{"name":"weloveayaka","email":"weloveayaka@gmail.com"}],"readme":"# @ayaka209/claude-context-mcp\n\n## ⚠️ COMMUNITY FORK NOTICE\n\n> **THIS IS NOT THE OFFICIAL CLAUDE CONTEXT MCP SERVER**\n>\n> This is a **community fork** building upon the excellent work of the original Zilliz team. We have deep respect for the original authors.\n>\n> **🙏 Please support the official version first**: [@zilliz/code-context-mcp](https://www.npmjs.com/package/@zilliz/code-context-mcp)\n>\n> **About this fork:**\n> - Published under `@ayaka209/*` namespace to avoid conflicts\n> - Experimental features and community contributions\n> - Not affiliated with or endorsed by the original Zilliz team\n>\n> **⚠️ For production use, we recommend the official package**\n\n---\n\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/@ayaka209/claude-context-mcp.svg)](https://www.npmjs.com/package/@ayaka209/claude-context-mcp)\n[![npm downloads](https://img.shields.io/npm/dm/@ayaka209/claude-context-mcp.svg)](https://www.npmjs.com/package/@ayaka209/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## 🚀 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## Quick Start\n\n### Prerequisites\n\nBefore using the MCP server, make sure you have:\n\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> 📋 **Quick Reference**: For a complete list of environment variables and their descriptions, see the [Environment Variables Guide](../../docs/getting-started/environment-variables.md).\n\n```bash\n# Supported providers: OpenAI, AzureOpenAI, 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:**\nSee `getSupportedModels` in [`openai-embedding.ts`](https://github.com/zilliztech/claude-context/blob/master/packages/core/src/embedding/openai-embedding.ts) for the full list of supported models.\n\n**Getting API Key:**\n\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. Azure OpenAI Configuration</strong></summary>\n\nAzure OpenAI provides the same high-quality embeddings as OpenAI through your Azure infrastructure.\n\n```bash\n# Required: Your Azure OpenAI configuration\nEMBEDDING_PROVIDER=AzureOpenAI\nAZURE_OPENAI_API_KEY=your-azure-api-key\nAZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com\nAZURE_OPENAI_DEPLOYMENT_NAME=text-embedding-3-small-deployment\n\n# Optional: API version (default: 2024-02-01)\nAZURE_OPENAI_API_VERSION=2024-02-01\n```\n\n**Configuration Requirements:**\n- `AZURE_OPENAI_ENDPOINT`: Your Azure OpenAI resource endpoint\n- `AZURE_OPENAI_DEPLOYMENT_NAME`: Your deployment name (not model name)\n- `AZURE_OPENAI_API_KEY`: Your Azure OpenAI API key\n\n**Getting Started with Azure OpenAI:**\n1. Create an Azure OpenAI resource in [Azure Portal](https://portal.azure.com)\n2. Deploy an embedding model (e.g., text-embedding-3-small)\n3. Copy your endpoint URL and API key from Azure Portal\n4. Use the deployment name you created (not the model name)\n\n</details>\n\n<details>\n<summary><strong>3. 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:**\nSee `getSupportedModels` in [`voyageai-embedding.ts`](https://github.com/zilliztech/claude-context/blob/master/packages/core/src/embedding/voyageai-embedding.ts) for the full list of supported models.\n\n**Getting API Key:**\n\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>4. 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# Optional: Custom API base URL (for custom endpoints)\nGEMINI_BASE_URL=https://generativelanguage.googleapis.com/v1beta\n```\n\n**Available Models:**\nSee `getSupportedModels` in [`gemini-embedding.ts`](https://github.com/zilliztech/claude-context/blob/master/packages/core/src/embedding/gemini-embedding.ts) for the full list of supported models.\n\n**Getting API Key:**\n\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>5. 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**Setup Instructions:**\n\n1. Install Ollama from [ollama.ai](https://ollama.ai/)\n2. Pull the embedding model:\n\n   ```bash\n   ollama pull nomic-embed-text\n   ```\n\n3. Ensure Ollama is running:\n\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#### Embedding Batch Size\n\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\n```bash\nEMBEDDING_BATCH_SIZE=512\n```\n\n#### Custom File Processing (Optional)\n\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<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 @ayaka209/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>OpenAI Codex CLI</strong></summary>\n\nCodex CLI uses TOML configuration files:\n\n1. Create or edit the `~/.codex/config.toml` file.\n\n2. Add the following configuration:\n\n```toml\n# IMPORTANT: the top-level key is `mcp_servers` rather than `mcpServers`.\n[mcp_servers.claude-context]\ncommand = \"npx\"\nargs = [\"@ayaka209/claude-context-mcp@latest\"]\nenv = { \"OPENAI_API_KEY\" = \"your-openai-api-key\", \"MILVUS_TOKEN\" = \"your-zilliz-cloud-api-key\" }\n# Optional: override the default 10s startup timeout\nstartup_timeout_ms = 20000\n```\n\n3. Save the file and restart Codex CLI to apply the changes.\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\": [\"@ayaka209/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>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\": [\"@ayaka209/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\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@ayaka209/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**Azure OpenAI Configuration:**\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@ayaka209/claude-context-mcp@latest\"],\n      \"env\": {\n        \"EMBEDDING_PROVIDER\": \"AzureOpenAI\",\n        \"AZURE_OPENAI_API_KEY\": \"your-azure-api-key\",\n        \"AZURE_OPENAI_ENDPOINT\": \"https://your-resource.openai.azure.com\",\n        \"AZURE_OPENAI_DEPLOYMENT_NAME\": \"text-embedding-3-small-deployment\",\n        \"MILVUS_TOKEN\": \"your-zilliz-cloud-api-key\"\n      }\n    }\n  }\n}\n```\n\n**VoyageAI Configuration:**\n\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@ayaka209/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\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@ayaka209/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\n```json\n{\n  \"mcpServers\": {\n    \"claude-context\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@ayaka209/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<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\", \"@ayaka209/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\": [\"@ayaka209/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>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\", \"@ayaka209/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\", \"@ayaka209/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**: `[\"@ayaka209/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\": [\"@ayaka209/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 @ayaka209/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\", \"@ayaka209/claude-context-mcp@latest\"] \n    } \n  ] \n}\n```\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\": [\"@ayaka209/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>Zencoder</strong></summary>\n\nZencoder offers support for MCP tools and servers in both its JetBrains and VS Code plugin versions.\n\n1. Go to the Zencoder menu (...)\n2. From the dropdown menu, select `Tools`\n3. Click on the `Add Custom MCP`\n4. Add the name (i.e. `Claude Context` and server configuration from below, and make sure to hit the `Install` button\n\n```json\n{\n    \"command\": \"npx\",\n    \"args\": [\"@ayaka209/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\n5. Save the server by hitting the `Install` button.\n\n</details>\n\n<details>\n<summary><strong>LangChain/LangGraph</strong></summary>\n\nFor LangChain/LangGraph integration examples, see [this example](https://github.com/zilliztech/claude-context/blob/643796a0d30e706a2a0dff3d55621c9b5d831807/evaluation/retrieval/custom.py#L88).\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 @ayaka209/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- 🔍 **Hybrid Code Search**: Natural language queries using advanced hybrid search (BM25 + dense vector) to find relevant code snippets\n- 📁 **Codebase Indexing**: Index entire codebases for fast hybrid search across millions of lines of code\n- 🔄 **Incremental Indexing**: Efficiently re-index only changed files using Merkle trees for auto-sync\n- 🧩 **Intelligent Code Chunking**: AST-based code analysis for syntax-aware chunking with automatic fallback\n- 🗄️ **Scalable**: Integrates with Zilliz Cloud for scalable vector search, no matter how large your codebase is\n- 🛠️ **Customizable**: Configure file extensions, ignore patterns, and embedding models\n- ⚡ **Real-time**: Interactive indexing and searching with progress feedback\n\n## Available Tools\n\n### 1. `index_codebase`\n\nIndex a codebase directory for hybrid search (BM25 + dense vector).\n\n**Parameters:**\n\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`\n\nSearch the indexed codebase using natural language queries with hybrid search (BM25 + dense vector).\n\n**Parameters:**\n\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- `extensionFilter` (optional): List of file extensions to filter results (e.g., ['.ts', '.py']) (default: [])\n\n### 3. `clear_index`\n\nClear the search index for a specific codebase.\n\n**Parameters:**\n\n- `path` (required): Absolute path to the codebase directory to clear index for\n\n### 4. `get_indexing_status`\n\nGet the current indexing status of a codebase. Shows progress percentage for actively indexing codebases and completion status for indexed codebases.\n\n**Parameters:**\n\n- `path` (required): Absolute path to the codebase directory to check status for\n\n## Contributing\n\nThis package is part of the Claude Context monorepo. Please see:\n\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- **[@ayaka209/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\n","readmeFilename":"README.md"}