🆕 Looking for persistent memory for Claude Code? Check out memsearch Claude Code plugin — a markdown-first memory system that gives your AI agent long-term memory across sessions.
Claude Context is an MCP plugin that adds semantic code search to Claude Code and other AI coding agents, giving them deep context from your entire codebase.
🧠 Your Entire Codebase as Context: Claude Context uses semantic search to find all relevant code from millions of lines. No multi-round discovery needed. It brings results straight into the Claude's context.
💰 Cost-Effective for Large Codebases: Instead of loading entire directories into Claude for every request, which can be very expensive, Claude Context efficiently stores your codebase in a vector database and only uses related code in context to keep your costs manageable.
Model Context Protocol (MCP) allows you to integrate Claude Context with your favorite AI coding assistants, e.g. Claude Code.
Get a free vector database on Zilliz Cloud 👈
Claude Context needs a vector database. You can sign up on Zilliz Cloud to get an API key.
Copy your Personal Key to replace your-zilliz-cloud-api-key in the configuration examples.
Get OpenAI API Key for embedding model
You need an OpenAI API key for the embedding model. You can get one by signing up at OpenAI.
Your API key will look like this: it always starts with sk-.
Copy your key and use it in the configuration examples below as your-openai-api-key.
System Requirements:
- Node.js >= 20.0.0
Use the command line interface to add the Claude Context MCP server:
claude mcp add claude-context \
-e OPENAI_API_KEY=sk-your-openai-api-key \
-e MILVUS_ADDRESS=your-zilliz-cloud-public-endpoint \
-e MILVUS_TOKEN=your-zilliz-cloud-api-key \
-- npx @zilliz/claude-context-mcp@latestSee the Claude Code MCP documentation for more details about MCP server management.
OpenAI Codex CLI
Codex CLI uses TOML configuration files:
-
Create or edit the
~/.codex/config.tomlfile. -
Add the following configuration:
# IMPORTANT: the top-level key is `mcp_servers` rather than `mcpServers`.
[mcp_servers.claude-context]
command = "npx"
args = ["@zilliz/claude-context-mcp@latest"]
env = { "OPENAI_API_KEY" = "your-openai-api-key", "MILVUS_TOKEN" = "your-zilliz-cloud-api-key" }
# Optional: override the default 10s startup timeout
startup_timeout_ms = 20000- Save the file and restart Codex CLI to apply the changes.
Gemini CLI
Gemini CLI requires manual configuration through a JSON file:
- Create or edit the
~/.gemini/settings.jsonfile. - Add the following configuration:
{
"mcpServers": {
"claude-context": {
"command": "npx",
"args": ["@zilliz/claude-context-mcp@latest"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
}
}- Save the file and restart Gemini CLI to apply the changes.
Qwen Code
Create or edit the ~/.qwen/settings.json file and add the following configuration:
{
"mcpServers": {
"claude-context": {
"command": "npx",
"args": ["@zilliz/claude-context-mcp@latest"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
}
}Cursor
Go to: Settings -> Cursor Settings -> MCP -> Add new global MCP server
Pasting 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 for more info.
{
"mcpServers": {
"claude-context": {
"command": "npx",
"args": ["-y", "@zilliz/claude-context-mcp@latest"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
}
}Void
Go to: Settings -> MCP -> Add MCP Server
Add the following configuration to your Void MCP settings:
{
"mcpServers": {
"code-context": {
"command": "npx",
"args": ["-y", "@zilliz/claude-context-mcp@latest"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
}
}Claude Desktop
Add to your Claude Desktop configuration:
{
"mcpServers": {
"claude-context": {
"command": "npx",
"args": ["@zilliz/claude-context-mcp@latest"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
}
}Windsurf
Windsurf supports MCP configuration through a JSON file. Add the following configuration to your Windsurf MCP settings:
{
"mcpServers": {
"claude-context": {
"command": "npx",
"args": ["-y", "@zilliz/claude-context-mcp@latest"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
}
}VS Code
The Claude Context MCP server can be used with VS Code through MCP-compatible extensions. Add the following configuration to your VS Code MCP settings:
{
"mcpServers": {
"claude-context": {
"command": "npx",
"args": ["-y", "@zilliz/claude-context-mcp@latest"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
}
}Cherry Studio
Cherry 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:
- Navigate to Settings → MCP Servers → Add Server.
- Fill in the server details:
- Name:
claude-context - Type:
STDIO - Command:
npx - Arguments:
["-y", "@zilliz/claude-context-mcp@latest"] - Environment Variables:
OPENAI_API_KEY:your-openai-api-keyMILVUS_ADDRESS:your-zilliz-cloud-public-endpointMILVUS_TOKEN:your-zilliz-cloud-api-key
- Name:
- Save the configuration to activate the server.
Cline
Cline uses a JSON configuration file to manage MCP servers. To integrate the provided MCP server configuration:
-
Open Cline and click on the MCP Servers icon in the top navigation bar.
-
Select the Installed tab, then click Advanced MCP Settings.
-
In the
cline_mcp_settings.jsonfile, add the following configuration:
{
"mcpServers": {
"claude-context": {
"command": "npx",
"args": ["@zilliz/claude-context-mcp@latest"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
}
}- Save the file.
Augment
To configure Claude Context MCP in Augment Code, you can use either the graphical interface or manual configuration.
-
Click the hamburger menu.
-
Select Settings.
-
Navigate to the Tools section.
-
Click the + Add MCP button.
-
Enter the following command:
npx @zilliz/claude-context-mcp@latest -
Name the MCP: Claude Context.
-
Click the Add button.
- Press Cmd/Ctrl Shift P or go to the hamburger menu in the Augment panel
- Select Edit Settings
- Under Advanced, click Edit in settings.json
- Add the server configuration to the
mcpServersarray in theaugment.advancedobject
"augment.advanced": {
"mcpServers": [
{
"name": "claude-context",
"command": "npx",
"args": ["-y", "@zilliz/claude-context-mcp@latest"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
]
}Roo Code
Roo Code utilizes a JSON configuration file for MCP servers:
-
Open Roo Code and navigate to Settings → MCP Servers → Edit Global Config.
-
In the
mcp_settings.jsonfile, add the following configuration:
{
"mcpServers": {
"claude-context": {
"command": "npx",
"args": ["@zilliz/claude-context-mcp@latest"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
}
}- Save the file to activate the server.
Zencoder
Zencoder offers support for MCP tools and servers in both its JetBrains and VS Code plugin versions.
- Go to the Zencoder menu (...)
- From the dropdown menu, select
Tools - Click on the
Add Custom MCP - Add the name (i.e.
Claude Contextand server configuration from below, and make sure to hit theInstallbutton
{
"command": "npx",
"args": ["@zilliz/claude-context-mcp@latest"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
- Save the server by hitting the
Installbutton.
LangChain/LangGraph
For LangChain/LangGraph integration examples, see this example.
Other MCP Clients
The server uses stdio transport and follows the standard MCP protocol. It can be integrated with any MCP-compatible client by running:
npx @zilliz/claude-context-mcp@latest-
Open Claude Code
cd your-project-directory claude -
Index your codebase:
Index this codebase -
Check indexing status:
Check the indexing status -
Start searching:
Find functions that handle user authentication
🎉 That's it! You now have semantic code search in Claude Code.
For more detailed MCP environment variable configuration, see our Environment Variables Guide.
To configure custom embedding models (e.g., text-embedding-3-large for OpenAI, voyage-code-3 for VoyageAI), see the MCP Configuration Examples for detailed setup instructions for each provider.
For detailed explanation of file inclusion and exclusion rules, and how to customize them, see our File Inclusion & Exclusion Rules.
Index a codebase directory for hybrid search (BM25 + dense vector).
Search the indexed codebase using natural language queries with hybrid search (BM25 + dense vector).
Clear the search index for a specific codebase.
Get the current indexing status of a codebase. Shows progress percentage for actively indexing codebases and completion status for indexed codebases.
Our controlled evaluation demonstrates that Claude Context MCP achieves ~40% token reduction under the condition of equivalent retrieval quality. This translates to significant cost and time savings in production environments. This also means that, under the constraint of limited token context length, using Claude Context yields better retrieval and answer results.
For detailed evaluation methodology and results, see the evaluation directory.
- 🔍 Hybrid Code Search: Ask questions like "find functions that handle user authentication" and get relevant, context-rich code instantly using advanced hybrid search (BM25 + dense vector).
- 🧠 Context-Aware: Discover large codebase, understand how different parts of your codebase relate, even across millions of lines of code.
- ⚡ Incremental Indexing: Efficiently re-index only changed files using Merkle trees.
- 🧩 Intelligent Code Chunking: Analyze code in Abstract Syntax Trees (AST) for chunking.
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