Model Context Protocol (MCP)

Overview

The Model Context Protocol (MCP) is an open standard that allows AI models to interact with external tools and data sources. Jan implements MCP as a host, letting you connect your models to databases, APIs, web services, and custom tools through a unified interface.

MCP acts as a universal adapter between AI models and external tools, eliminating the need for custom integrations for each tool-model combination.

Why Use MCP?

Standardized Integration

One protocol for all tools - no custom connectors needed for each model-tool pair.

Extensible Capabilities

Give models access to real-time data, search engines, databases, and custom APIs.

Modular & Flexible

Swap models or tools without changing your integration code.

Open Ecosystem

Use community-built MCP servers or create your own custom tools.

How MCP Works

MCP uses a client-server architecture:

Jan (MCP Host): Coordinates between your model and MCP servers
MCP Servers: Provide tools, resources, and prompts
AI Model: Decides when and how to use available tools

Not all models support tool calling. Verify your model is MCP-compatible before setting up integrations. Cloud models like GPT-4 and Claude work well. Local models need tool calling enabled in their capabilities.

Quick Start

Prerequisites

Enable Experimental Features

Go to Settings > General > Advanced
Enable Experimental Features
Restart Jan if prompted

Install Dependencies

MCP servers require Node.js or Python:

Download Node.js (v18 or later)
Download Python (3.10 or later)

Verify installation:

node --version  # Should show v18+
python --version  # Should show 3.10+

Enable MCP Permissions

Navigate to Settings > MCP Servers
Toggle Allow All MCP Tool Permission ON
This lets models use MCP tools without prompting each time

Example: Browser MCP Setup

Let’s set up the Browser MCP server to give your models web browsing capabilities:

Add MCP Server

Go to Settings > MCP Servers
Click the + button in the upper right
Enter configuration:
- Server Name: browsermcp
- Command: npx
- Arguments: @browsermcp/mcp
- Environment Variables: Leave empty
Click Save

Install Browser Extension

Open a Chromium browser (Chrome, Brave, Edge, Vivaldi)
Visit Browser MCP Extension
Click Add to Chrome/Browser
Enable the extension in incognito/private windows
Click the extension icon and connect to the MCP server

Enable Model Tool Calling

For Cloud Models (e.g., Claude):

Go to Settings > Model Providers > Anthropic
After entering your API key, click the + or edit button next to your model
Enable Tools

For Local Models:

Go to Settings > Model Providers > Llama.cpp
Click the edit button next to your model
Enable Tools capability

Test the Integration

Create a new chat
Select a model with tools enabled (e.g., Claude Sonnet 4)
Ask: “Visit github.com and tell me what’s trending”
Watch the model use the browser tool to complete the task

Popular MCP Servers

Search & Research

Serper

Google search integration with 2,500 free searches/month

Exa

AI-powered semantic search engine

Browser MCP

Automate web browsing and data extraction

Octagon

Deep research assistant for comprehensive analysis

Productivity

Todoist

Task management and to-do lists

Linear

Issue tracking and project management

Canva

Design and content creation

Data & Development

Jupyter

Execute Python code in Jupyter notebooks

E2B

Secure code execution sandbox

Adding MCP Servers

Configuration Format

All MCP servers follow this configuration pattern:

Field	Description	Example
Server Name	Unique identifier for the server	`serper`, `browsermcp`
Command	Executable to run the server	`npx`, `python`, `node`
Arguments	Command arguments	`@serper-mcp/server`, `-m jupyter`
Environment Variables	Key-value pairs for configuration	`API_KEY=your-key-here`

NPM-based Servers

Many MCP servers are distributed via npm:

Command: npx
Arguments: @package-name/server

Example - Serper Search:

Server Name: serper
Command: npx
Arguments: @serper-mcp/server
Environment Variables: SERPER_API_KEY=your_api_key

Python-based Servers

Some servers use Python:

Command: python
Arguments: -m module_name

Example - Jupyter:

Server Name: jupyter
Command: python
Arguments: -m jupyter_mcp
Environment Variables: (optional)

Local Script Servers

Run custom scripts as MCP servers:

Command: node
Arguments: /path/to/your/server.js

Command: python
Arguments: /path/to/your/server.py

Model Compatibility

Verifying Tool Calling Support

Cloud Models

Most cloud models support tool calling:✅ Fully Supported:

OpenAI: GPT-4o, GPT-4 Turbo, GPT-4, GPT-3.5 Turbo
Anthropic: Claude Opus 4, Sonnet 4, Haiku 3.5
Google: Gemini Pro, Gemini Ultra
Mistral: Large, Medium models

⚠️ Limited/No Support:

Older GPT-3 models
Some specialized models

Enable Tools:

Go to Settings > Model Providers > [Provider]
Click edit or + next to your model
Enable Tools capability

Local Models

Tool calling support varies widely for local models:✅ Good Tool Calling:

Llama 3.3 70B
Hermes 2 Pro (Mistral/Llama variants)
Jan v1 (optimized for tool calling)
Functionary models
NousResearch Hermes

⚠️ Limited Support:

Smaller models (< 7B parameters)
Base models (non-instruct versions)
Older model architectures

Enable Tools:

Go to Settings > Model Providers > Llama.cpp
Click the edit button next to your model
Enable Tools capability
Test with simple tool calling tasks first

For best results with MCP tools, use models specifically trained for tool calling like Jan v1, Claude Sonnet 4, or GPT-4o.

Managing MCP Servers

View Active Servers

Go to Settings > MCP Servers to see:

Server name and status (connected/disconnected)
Available tools from each server
Resource count and types
Connection logs

Enable/Disable Servers

Toggle servers on/off without deleting configuration:

Find the server in Settings > MCP Servers
Use the toggle switch to enable/disable
Disabled servers won’t load or consume resources

Update Server Configuration

Click the edit icon next to a server
Modify settings (command, arguments, environment variables)
Save changes
Restart the server for changes to take effect

Remove Servers

Find the server in Settings > MCP Servers
Click the delete icon or three dots menu
Confirm deletion

Security Considerations

MCP gives models access to external tools and data. Review these security guidelines carefully.

Permission Model

Allow All MCP Tool Permission:

✅ Convenient - models can use tools automatically
⚠️ Less secure - no per-request approval
Best for: Trusted models, local development, single-user setups

Manual Approval (coming soon):

✅ More secure - approve each tool use
⚠️ Less convenient - interrupts workflow
Best for: Sensitive data, production systems, shared computers

Best Practices

Vet MCP Servers

Only install MCP servers from trusted sources. Review code when possible.

Use Environment Variables

Store API keys in MCP server environment variables, not in prompts or model configs.

Limit API Access

Use API keys with minimal required permissions. Create read-only keys when possible.

Monitor Usage

Check server logs regularly for unexpected tool usage or errors.

Sandbox Sensitive Data

Test MCP servers with non-sensitive data before using with production systems.

Prompt Injection Risks

MCP servers can be vulnerable to prompt injection:

Malicious input could trick models into misusing tools
External data (web pages, documents) may contain adversarial prompts
Models might perform unintended actions

Mitigation:

Use models less susceptible to prompt injection (GPT-4, Claude)
Review tool calls in server logs
Implement rate limiting on sensitive APIs
Use read-only access when possible

Building Custom MCP Servers

MCP Server Basics

An MCP server can provide:

Tools: Functions the model can call (e.g., search, calculate, query database)
Resources: Data the model can access (e.g., files, databases, APIs)
Prompts: Pre-built prompt templates for common tasks

Quick Example (Node.js)

import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';

const server = new Server(
  {
    name: 'example-server',
    version: '1.0.0',
  },
  {
    capabilities: {
      tools: {},
    },
  }
);

// Define a tool
server.setRequestHandler('tools/list', async () => {
  return {
    tools: [
      {
        name: 'get_weather',
        description: 'Get current weather for a location',
        inputSchema: {
          type: 'object',
          properties: {
            location: {
              type: 'string',
              description: 'City name',
            },
          },
          required: ['location'],
        },
      },
    ],
  };
});

// Handle tool calls
server.setRequestHandler('tools/call', async (request) => {
  if (request.params.name === 'get_weather') {
    const location = request.params.arguments.location;
    // Your weather API logic here
    return {
      content: [
        {
          type: 'text',
          text: `Weather in ${location}: Sunny, 72°F`,
        },
      ],
    };
  }
});

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Resources

Troubleshooting

MCP Server Won't Connect

Symptoms: Server shows as disconnected in settingsSolutions:

Verify Node.js or Python is installed correctly
Check command and arguments for typos
Review server logs in Jan for error messages
Ensure required npm packages are accessible: npx @package-name/server --help
Restart Jan completely

Model Doesn't Use Tools

Symptoms: Model ignores available MCP toolsSolutions:

Verify Tools capability is enabled for the model
Ensure the model supports tool calling (try Claude or GPT-4)
Check that “Allow All MCP Tool Permission” is ON
Be explicit in your prompt: “Use the search tool to find…”
Try a different model known for good tool calling

Tools Fail to Execute

Symptoms: Model tries to use tool but gets errorsSolutions:

Check environment variables (API keys, config)
Review server logs for specific error messages
Verify API keys have sufficient credits/permissions
Test the tool independently (e.g., curl for API-based tools)
Ensure network connectivity for cloud-based tools

Vision Models Not Working with MCP

Symptoms: Browser MCP or screenshot tools failSolutions:

Verify your model supports vision/images (not just tool calling)
Enable Vision capability in model settings
Use models known for vision: GPT-4o, Claude 4, Gemini Pro
Check that image data is being passed correctly in server logs

Environment Variables Not Loading

Symptoms: MCP server can’t access API keys or configSolutions:

Check format: KEY=value (no quotes, no spaces around =)
Use multiple lines for multiple variables
Restart the MCP server after changing variables
Verify variable names match server’s requirements
Check server documentation for required variables

Advanced Usage

Context Management

MCP servers consume context window space:

Each active tool adds to context overhead
Large tool responses count toward token limits
Multiple MCP servers = more context usage

Optimization Tips:

Only enable servers you’re actively using
Use models with larger context windows (32k+) for multiple tools
Disable tools after completing relevant tasks
Monitor context usage in conversation

Chaining Tools

Models can chain multiple MCP tools together:

User: "Research the top 3 AI companies and create a summary document"

Model:
1. Uses Serper MCP to search for "top AI companies"
2. Uses Browser MCP to visit company websites
3. Uses File System MCP to create summary document
4. Returns result to user

Use models with strong reasoning capabilities (GPT-4o, Claude Opus) for complex multi-tool workflows.

Resource Access

Some MCP servers provide resources (not just tools):

File system access
Database connections
Document repositories
API endpoints

Models can query these resources directly for context.

Next Steps

MCP Examples

Step-by-step guides for popular MCP servers

Model Parameters

Optimize models for better tool calling

Local Models

Find models with strong tool calling support

API Server

Use MCP-enabled models via API

Get Started

Desktop App

Features

Integrations

​Overview

​Why Use MCP?

Standardized Integration

Extensible Capabilities

Modular & Flexible

Open Ecosystem

​How MCP Works

​Quick Start

​Prerequisites

​Example: Browser MCP Setup

​Popular MCP Servers

​Search & Research

Serper

Exa

Browser MCP

Octagon

​Productivity

Todoist

Linear

Canva

​Data & Development

Jupyter

E2B

​Adding MCP Servers

​Configuration Format

​NPM-based Servers

​Python-based Servers

​Local Script Servers

​Model Compatibility

​Verifying Tool Calling Support

​Managing MCP Servers

​View Active Servers

​Enable/Disable Servers

​Update Server Configuration

​Remove Servers

​Security Considerations

​Permission Model

​Best Practices

​Prompt Injection Risks

​Building Custom MCP Servers

​MCP Server Basics

​Quick Example (Node.js)

​Resources

​Troubleshooting

​Advanced Usage

​Context Management

​Chaining Tools

​Resource Access

​Next Steps

MCP Examples

Model Parameters

Local Models

API Server

Build docs developers (and LLMs) love

Overview

Why Use MCP?

How MCP Works

Quick Start

Prerequisites

Example: Browser MCP Setup

Popular MCP Servers

Search & Research

Productivity

Data & Development

Adding MCP Servers

Configuration Format

NPM-based Servers

Python-based Servers

Local Script Servers

Model Compatibility

Verifying Tool Calling Support

Managing MCP Servers

View Active Servers

Enable/Disable Servers

Update Server Configuration

Remove Servers

Security Considerations

Permission Model

Best Practices

Prompt Injection Risks

Building Custom MCP Servers

MCP Server Basics

Quick Example (Node.js)

Resources

Troubleshooting

Advanced Usage

Context Management

Chaining Tools

Resource Access

Next Steps