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Llama.cpp Bridge▌

by openconstruct

Llama.cpp Bridge connects local llama-server instances to MCP clients, enabling chat, health checks, and flexible llama.

Bridges local llama-server instances with MCP clients, providing chat interface, health monitoring, and configurable generation parameters for integrating llama.cpp models with desktop applications

github stars

★ 8

0 commentsdiscussion

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

No cloud API keys requiredFull conversation supportBuilt-in testing tools

best for

/ AI researchers running local models
/ Privacy-focused users avoiding cloud APIs
/ Developers integrating local LLMs with desktop workflows

capabilities

/ Chat with local llama.cpp models through Claude Desktop
/ Control generation parameters like temperature and max_tokens
/ Monitor llama-server health and status
/ Track performance metrics and token usage
/ Test model capabilities with built-in tools

what it does

Connects Claude Desktop to your local llama.cpp models, letting you chat with local LLMs directly through Claude's interface.

about

Llama.cpp Bridge is a community-built MCP server published by openconstruct that provides AI assistants with tools and capabilities via the Model Context Protocol. Llama.cpp Bridge connects local llama-server instances to MCP clients, enabling chat, health checks, and flexible llama. It is categorized under ai ml, developer tools.

how to install

You can install Llama.cpp Bridge 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

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

readme

LibreModel MCP Server 🤖

A Model Context Protocol (MCP) server that bridges Claude Desktop with your local LLM instance running via llama-server.

Features

💬 Full conversation support with Local Model through Claude Desktop
🎛️ Complete parameter control (temperature, max_tokens, top_p, top_k)
✅ Health monitoring and server status checks
🧪 Built-in testing tools for different capabilities
📊 Performance metrics and token usage tracking
🔧 Easy configuration via environment variables

Quick Start

npm install @openconstruct/llama-mcp-server

A Model Context Protocol (MCP) server that bridges Claude Desktop with your local LLM instance running via llama-server.

Features

💬 Full conversation support with LibreModel through Claude Desktop
🎛️ Complete parameter control (temperature, max_tokens, top_p, top_k)
✅ Health monitoring and server status checks
🧪 Built-in testing tools for different capabilities
📊 Performance metrics and token usage tracking
🔧 Easy configuration via environment variables

Quick Start

1. Install Dependencies

cd llama-mcp
npm install

2. Build the Server

npm run build

3. Start Your LibreModel

Make sure llama-server is running with your model:

./llama-server -m lm37.gguf -c 2048 --port 8080

4. Configure Claude Desktop

Add this to your Claude Desktop configuration (~/.config/claude/claude_desktop_config.json):

{
  "mcpServers": {
    "libremodel": {
      "command": "node",
      "args": ["/home/jerr/llama-mcp/dist/index.js"]
    }
  }
}

5. Restart Claude Desktop

Claude will now have access to LibreModel through MCP!

Usage

Once configured, you can use these tools in Claude Desktop:

💬 `chat` - Main conversation tool

Use the chat tool to ask LibreModel: "What is your name and what can you do?"

🧪 `quick_test` - Test LibreModel capabilities

Run a quick_test with type "creative" to see if LibreModel can write poetry

🏥 `health_check` - Monitor server status

Use health_check to see if LibreModel is running properly

Configuration

Set environment variables to customize behavior:

export LLAMA_SERVER_URL="http://localhost:8080"  # Default llama-server URL

Available Tools

Tool	Description	Parameters
`chat`	Converse with MOdel	`message`, `temperature`, `max_tokens`, `top_p`, `top_k`, `system_prompt`
`quick_test`	Run predefined capability tests	`test_type` (hello/math/creative/knowledge)
`health_check`	Check server health and status	None

Resources

Configuration: View current server settings
Instructions: Detailed usage guide and setup instructions

Development

# Install dependencies
npm install # LibreModel MCP Server 🤖

A Model Context Protocol (MCP) server that bridges Claude Desktop with your local LLM instance running via llama-server.

## Features

- 💬 **Full conversation support** with LibreModel through Claude Desktop
- 🎛️ **Complete parameter control** (temperature, max_tokens, top_p, top_k)
- ✅ **Health monitoring** and server status checks
- 🧪 **Built-in testing tools** for different capabilities
- 📊 **Performance metrics** and token usage tracking
- 🔧 **Easy configuration** via environment variables

## Quick Start

### 1. Install Dependencies

```bash
cd llama-mcp
npm install

2. Build the Server

npm run build

3. Start Your LibreModel

Make sure llama-server is running with your model:

./llama-server -m lm37.gguf -c 2048 --port 8080

4. Configure Claude Desktop

Add this to your Claude Desktop configuration (~/.config/claude/claude_desktop_config.json):

{
  "mcpServers": {
    "libremodel": {
      "command": "node",
      "args": ["/home/jerr/llama-mcp/dist/index.js"]
    }
  }
}

5. Restart Claude Desktop

Claude will now have access to LibreModel through MCP!

Usage

Once configured, you can use these tools in Claude Desktop:

💬 `chat` - Main conversation tool

Use the chat tool to ask LibreModel: "What is your name and what can you do?"

🧪 `quick_test` - Test LibreModel capabilities

Run a quick_test with type "creative" to see if LibreModel can write poetry

🏥 `health_check` - Monitor server status

Use health_check to see if LibreModel is running properly

Configuration

Set environment variables to customize behavior:

export LLAMA_SERVER_URL="http://localhost:8080"  # Default llama-server URL

Available Tools

Tool	Description	Parameters
`chat`	Converse with MOdel	`message`, `temperature`, `max_tokens`, `top_p`, `top_k`, `system_prompt`
`quick_test`	Run predefined capability tests	`test_type` (hello/math/creative/knowledge)
`health_check`	Check server health and status	None

Resources

Configuration: View current server settings
Instructions: Detailed usage guide and setup instructions

Development

# Install dependencies
npm install openconstruct/llama-mcp-server

# Development mode (auto-rebuild)
npm run dev

# Build for production
npm run build

# Start the server directly
npm start

Architecture

Claude Desktop ←→ LLama MCP Server ←→ llama-server API ←→ Local Model

The MCP server acts as a bridge, translating MCP protocol messages into llama-server API calls and formatting responses for Claude Desktop.

Troubleshooting

"Cannot reach LLama server"

Ensure llama-server is running on the configured port
Check that the model is loaded and responding
Verify firewall/network settings

"Tool not found in Claude Desktop"

Restart Claude Desktop after configuration changes
Check that the path to index.js is correct and absolute
Verify the MCP server builds without errors

Poor response quality

Adjust temperature and sampling parameters
Try different system prompts

License

CC0-1.0 - Public Domain. Use freely!

Built with ❤️ for open-source AI and the LibreModel project. by Claude Sonnet4

Development mode (auto-rebuild)

npm run dev

Build for production

npm run build

Start the server directly

npm start


## Architecture

Claude Desktop ←→ LLama MCP Server ←→ llama-server API ←→ Local Model


The MCP server acts as a bridge, translating MCP protocol messages into llama-server API calls and formatting responses for Claude Desktop.

## Troubleshooting

**"Cannot reach LLama server"**
- Ensure llama-server is running on the configured port
- Check that the model is loaded and responding
- Verify firewall/network settings

**"Tool not found in Claude Desktop"**
- Restart Claude Desktop after configuration changes
- Check that the path to `index.js` is correct and absolute
- Verify the MCP server builds without errors

**Poor response quality**
- Adjust temperature and sampling parameters
- Try different system prompts

## License

CC0-1.0 - Public Domain. Use freely!

---

Built with ❤️ for open-source AI and the LibreModel project. by Claude Sonnet4

### 1. Install Dependencies

```bash
cd llama-mcp
npm install

2. Build the Server

npm run build

3. Start Your LibreModel

Make sure llama-server is running with your model:

./llama-server -m lm37.gguf -c 2048 --port 8080

4. Configure Claude Desktop

Add this to your Claude Desktop configuration (~/.config/claude/claude_desktop_config.json):

{
  "mcpServers": {
    "libremodel": {
      "command": "node",
      "args": ["/home/jerr/llama-mcp/dist/index.js"]
    }
  }
}

5. Restart Claude Desktop

Claude will now have access to LibreModel through MCP!

Usage

Once configured, you can use these tools in Claude Desktop:

💬 `chat` - Main conversation tool

Use the chat tool to ask LibreModel: "What is your name and what can you do?"

🧪 `quick_test` - Test LibreModel capabilities

Run a quick_test with type "creative" to see if LibreModel can write poetry

🏥 `health_check` - Monitor server status

Use health_check to see if LibreModel is running properly

Configuration

Set environment variables to customize behavior:

export LLAMA_SERVER_URL="http://localhost:8080"  # Default llama-server URL

Available Tools

Tool	Description	Parameters
`chat`	Converse with MOdel	`message`, `temperature`, `max_tokens`, `top_p`, `top_k`, `system_prompt`
`quick_test`	Run predefined capability tests	`test_type` (hello/math/creative/knowledge)
`health_check`	Check server health and status	None

Resources

Configuration: View current server settings
Instructions: Detailed usage guide and setup instructions

Development

# Install dependencies
npm install

# Development mode (auto-rebuild)
npm run dev

# Build for production
npm run build

# Start the server directly
npm start

Architecture

Claude Desktop ←→ LLama MCP Server ←→ llama-server API ←→ Local Model

The MCP server acts as a bridge, translating MCP protocol messages into llama-server API calls and formatting responses for Claude Desktop.

Troubleshooting

"Cannot reach LLama server"

Ensure llama-server is running on the configured port
Check that the model is loaded and responding
Verify firewall/network settings

"Tool not found in Claude Desktop"

Restart Claude Desktop after configuration changes
Check that the path to index.js is correct and absolute
Verify the MCP server builds without errors

Poor response quality

Adjust temperature and sampling parameters
Try different system prompts

License

CC0-1.0 - Public Domain. Use freely!

Built with ❤️ for open-source AI and the LibreModel project. by Claude Sonnet

FAQ

What is the Llama.cpp Bridge MCP server?: Llama.cpp Bridge 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 Llama.cpp Bridge?: This profile displays 38 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.4 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.Install MCP server: npm install -g [package-name] or via GitHub
2.Add server configuration to ~/.claude/mcp.json
3.Provide required credentials and configuration
4.Restart Claude Desktop to load new server
5.Test basic functionality with simple prompts
6.Explore capabilities and experiment with use cases
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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MCP server reviews

Ratings

4.4★★★★★38 reviews

★★★★★Harper Johnson· Dec 20, 2024
Llama.cpp Bridge is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
★★★★★Olivia Abbas· Dec 12, 2024
Useful MCP listing: Llama.cpp Bridge is the kind of server we cite when onboarding engineers to host + tool permissions.
★★★★★Chaitanya Patil· Dec 8, 2024
Llama.cpp Bridge is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
★★★★★Piyush G· Nov 27, 2024
Useful MCP listing: Llama.cpp Bridge is the kind of server we cite when onboarding engineers to host + tool permissions.
★★★★★Isabella Sethi· Nov 11, 2024
Useful MCP listing: Llama.cpp Bridge is the kind of server we cite when onboarding engineers to host + tool permissions.
★★★★★Arya Reddy· Nov 3, 2024
Llama.cpp Bridge is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
★★★★★Arjun Anderson· Oct 22, 2024
We wired Llama.cpp Bridge into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
★★★★★Shikha Mishra· Oct 18, 2024
Llama.cpp Bridge reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
★★★★★Isabella Sharma· Oct 2, 2024
Llama.cpp Bridge reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
★★★★★Nia Gonzalez· Sep 21, 2024
Llama.cpp Bridge is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

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