developer-tools

Claude Context

zilliztech

by zilliztech

Claude Context offers semantic code search and indexing with vector embeddings and AST-based code splitting. Natural lan

Provides semantic code search and indexing using vector embeddings and AST-based code splitting, enabling natural language queries across codebases with automatic file filtering and support for multiple embedding providers and vector databases.

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Handles millions of lines of codeCost-effective alternative to loading full directoriesMultiple embedding and database providers

best for

  • / Developers working with large codebases
  • / AI coding agents needing codebase context
  • / Teams wanting cost-effective code search
  • / Projects requiring semantic code understanding

capabilities

  • / Search codebases using natural language queries
  • / Index code using vector embeddings and AST parsing
  • / Filter files automatically based on relevance
  • / Connect to multiple embedding providers
  • / Store embeddings in various vector databases

what it does

Adds semantic code search to Claude using vector embeddings, allowing natural language queries to find relevant code across large codebases without loading entire directories into context.

about

Claude Context is a community-built MCP server published by zilliztech that provides AI assistants with tools and capabilities via the Model Context Protocol. Claude Context offers semantic code search and indexing with vector embeddings and AST-based code splitting. Natural lan It is categorized under developer tools.

how to install

You can install Claude Context in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Claude Context is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Your entire codebase as Claude's context

License Node.js Documentation VS Code Marketplace npm - core npm - mcp Twitter DeepWiki <a href="https://discord.gg/mKc3R95yE5"><img height="20" src="https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white" alt="discord" /></a>

</div>

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.


🚀 Demo

img

Model Context Protocol (MCP) allows you to integrate Claude Context with your favorite AI coding assistants, e.g. Claude Code.

Quick Start

Prerequisites

<details> <summary>Get a free vector database on Zilliz Cloud 👈</summary>

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.

</details> <details> <summary>Get OpenAI API Key for embedding model</summary>

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.

</details>

Configure MCP for Claude Code

System Requirements:

  • Node.js >= 20.0.0 and < 24.0.0

Claude Context is not compatible with Node.js 24.0.0, you need downgrade it first if your node version is greater or equal to 24.

Configuration

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_TOKEN=your-zilliz-cloud-api-key \
  -- npx @zilliz/claude-context-mcp@latest

See the Claude Code MCP documentation for more details about MCP server management.

Other MCP Client Configurations

<details> <summary><strong>OpenAI Codex CLI</strong></summary>

Codex CLI uses TOML configuration files:

  1. Create or edit the ~/.codex/config.toml file.

  2. 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
  1. Save the file and restart Codex CLI to apply the changes.
</details> <details> <summary><strong>Gemini CLI</strong></summary>

Gemini CLI requires manual configuration through a JSON file:

  1. Create or edit the ~/.gemini/settings.json file.
  2. 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"
      }
    }
  }
}
  1. Save the file and restart Gemini CLI to apply the changes.
</details> <details> <summary><strong>Qwen Code</strong></summary>

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"
      }
    }
  }
}
</details> <details> <summary><strong>Cursor</strong></summary>

<a href="https://cursor.com/install-mcp?name=claude-context&config=JTdCJTIyY29tbWFuZCUyMiUzQSUyMm5weCUyMC15JTIwJTQwemlsbGl6JTJGY29kZS1jb250ZXh0LW1jcCU0MGxhdGVzdCUyMiUyQyUyMmVudiUyMiUzQSU3QiUyMk9QRU5BSV9BUElfS0VZJTIyJTNBJTIyeW91ci1vcGVuYWktYXBpLWtleSUyMiUyQyUyMk1JTFZVU19BRERSRVNTJTIyJTNBJTIybG9jYWxob3N0JTNBMTk1MzAlMjIlN0QlN0Q%3D"><img src="https://cursor.com/deeplink/mcp-install-dark.svg" alt="Add claude-context MCP server to Cursor" height="32" /></a>

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"
      }
    }
  }
}
</details> <details> <summary><strong>Void</strong></summary>

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"
      }
    }
  }
}
</details> <details> <summary><strong>Claude Desktop</strong></summary>

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"
      }
    }
  }
}
</details> <details> <summary><strong>Windsurf</strong></summary>

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"
      }
    }
  }
}
</details> <details> <summary><strong>VS Code</strong></summary>

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"
      }
    }
  }
}
</details> <details> <summary><strong>Cherry Studio</strong></summary>

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:

  1. Navigate to Settings → MCP Servers → Add Server.
  2. Fill in the server details:
    • Name: claude-context
    • Type: STDIO
    • Command: npx
    • Arguments: ["@zilliz/claude-context-mcp@latest"]
    • Environment Variables:
      • OPENAI_API_KEY: your-openai-api-key
      • MILVUS_ADDRESS: your-zilliz-cloud-public-endpoint
      • MILVUS_TOKEN: your-zilliz-cloud-api-key
  3. Save the configuration to activate the server.
</details> <details> <summary><strong>Cline</strong></summary>

Cline uses a JSON configuration file to manage MCP servers. To integrate the provided MCP server configuration:

  1. Open Cline and click on the MCP Servers icon in the top navigation bar.

  2. Select the Installed tab, then click Advanced MCP Settings.

  3. In the `cline_mcp_setting


FAQ

What is the Claude Context MCP server?
Claude Context is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Claude Context?
This profile displays 64 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.564 reviews
  • Shikha Mishra· Dec 28, 2024

    Strong directory entry: Claude Context surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Hassan Sanchez· Dec 20, 2024

    Claude Context has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Soo White· Dec 20, 2024

    According to our notes, Claude Context benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Omar Anderson· Dec 8, 2024

    Strong directory entry: Claude Context surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Diya Flores· Dec 4, 2024

    Claude Context is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Noah Desai· Nov 27, 2024

    Claude Context has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Diya Farah· Nov 23, 2024

    Claude Context reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Yash Thakker· Nov 19, 2024

    Claude Context has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Dev Okafor· Nov 11, 2024

    Strong directory entry: Claude Context surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Omar Malhotra· Nov 11, 2024

    I recommend Claude Context for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

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