Self-hosted MCP Gateway for your private AI agents
MCPJungle is a single source-of-truth registry for all Model Context Protocol Servers running in your Organisation.
🧑💻 Developers use it to register & manage MCP servers and the tools they provide from a central place.
🤖 MCP Clients use it to discover and consume all these tools from a single "Gateway" MCP Server.
MCPJungle is the only MCP Server your AI agents need to connect to!
- Developers using MCP Clients like Claude & Cursor that need to access MCP servers for tool-calling
- Developers building production-grade AI Agents that need to access MCP servers with built-in security, privacy and Access Control.
- Organisations wanting to view & manage all MCP client-server interactions from a central place. Hosted in their own datacenter 🔒
- Quick Start guide
- Installation
- Usage
- Limitations
- Contributing
This quickstart guide will show you how to:
- Start the MCPJungle server locally using docker-compose
- Register a simple MCP server in mcpjungle
- Connect your Claude to mcpjungle to access your MCP tools
curl -O https://raw.githubusercontent.com/mcpjungle/MCPJungle/refs/heads/main/docker-compose.yaml
docker-compose up -dDownload the client binary either using brew or from the Releases.
brew install mcpjungle/mcpjungle/mcpjungleAdd the context7 remote MCP server to mcpjungle
mcpjungle register --name context7 --url https://mcp.context7.com/mcpAdd the following configuration in your Claude MCP Servers:
{
"mcpServers": {
"mcpjungle": {
"command": "npx",
"args": [
"mcp-remote",
"http://localhost:8080/mcp",
"--allow-http"
]
}
}
}Try asking Claude for the following:
Use context7 to get the documentation for `/lodash/lodash`
Claude will then attempt to call the context7__get-library-docs tool via MCPJungle, which will return the documentation for the Lodash library.
Congratulations 🎉 You have successfully registered a remote MCP server in MCPJungle and called one of its tools via Claude
Warning
MCPJungle is BETA software.
We're actively working to make it production-ready. You can provide your feedback by starting a discussion in this repository.
MCPJungle is shipped as a stand-alone binary.
You can either download it from the Releases Page or use Homebrew to install it:
brew install mcpjungle/mcpjungle/mcpjungleVerify your installation by running
mcpjungle versionImportant
On MacOS, you will have to use homebrew because the compiled binary is not Notarized yet.
MCPJungle provides a Docker image which is useful for running the registry server (more about it later).
docker pull mcpjungle/mcpjungleMCPJungle has a Client-Server architecture and the binary lets you run both the Server and the Client.
The MCPJungle server is responsible for managing all the MCP servers registered in it and providing a unified MCP gateway for AI Agents to discover and call tools provided by these registered servers.
The gateway itself runs over streamable http transport and is accessible at the /mcp endpoint.
For running the MCPJungle server locally, docker compose is the recommended way:
curl -O https://raw.githubusercontent.com/mcpjungle/MCPJungle/refs/heads/main/docker-compose.yaml
docker-compose up -dThis will start the MCPJungle server along with a persistent Postgres database container.
You can quickly verify that the server is running:
curl http://localhost:8080/healthIf you plan on registering stdio-based MCP servers that rely on npx or uvx, use mcpjungle's stdio tagged docker image instead.
MCPJUNGLE_IMAGE_TAG=latest-stdio docker-compose up -dThis image is significantly larger. But it is very convenient and recommended for running locally when you rely on stdio-based MCP servers.
For example, if you only want to register remote mcp servers like context7 and deepwiki, you can use the standard (minimal) image.
But if you also want to use stdio-based servers like filesystem, time, github, etc., you should use the stdio-tagged image instead.
Note
If your stdio servers rely on tools other than npx or uvx, you will have to create a custom docker image that includes those dependencies along with the mcpjungle binary.
Production Deployment
The default MCPJungle Docker image is very lightweight - it only contains a minimal base image and the mcpjungle binary.
It is therefore suitable and recommended for production deployments.
For the database, we recommend you deploy a separate Postgres DB cluster and supply its endpoint to mcpjungle (see Database section below).
You can see the definitions of the standard Docker image and the stdio Docker image.
You can also run the server directly on your host machine using the binary:
mcpjungle startThis starts the main registry server and MCP gateway, accessible on port 8080 by default.
The mcpjungle server relies on a database and by default, creates a SQLite DB in the current working directory.
This is okay when you're just testing things out locally.
Alternatively, you can supply a DSN for a Postgresql database to the server:
export DATABASE_URL=postgres://admin:root@localhost:5432/mcpjungle_db
#run as container
docker run mcpjungle/mcpjungle:latest
# or run directly
mcpjungle startOnce the server is up, you can use the mcpjungle CLI to interact with it.
MCPJungle currently supports MCP servers using stdio and Streamable HTTP Transports.
Let's see how to register them in mcpjungle.
Let's say you're already running a streamable http MCP server locally at http://127.0.0.1:8000/mcp which provides basic math tools like add, subtract, etc.
You can register this MCP server with MCPJungle:
mcpjungle register --name calculator --description "Provides some basic math tools" --url http://127.0.0.1:8000/mcpIf you used docker-compose to run the server, and you're not on Linux, you will have to use host.docker.internal instead of your local loopback address.
mcpjungle register --name calculator --description "Provides some basic math tools" --url http://host.docker.internal:8000/mcpThe registry will now start tracking this MCP server and load its tools.
You can also provide a configuration file to register the MCP server:
cat ./calculator.json
{
"name": "calculator",
"transport": "streamable_http",
"description": "Provides some basic math tools",
"url": "http://127.0.0.1:8000/mcp"
}
mcpjungle register -c ./calculator.jsonAll tools provided by this server are now accessible via MCPJungle:
mcpjungle list tools
# Check tool usage
mcpjungle usage calculator__multiply
# Call a tool
mcpjungle invoke calculator__multiply --input '{"a": 100, "b": 50}'
Note
A tool in MCPJungle must be referred to by its canonical name which follows the pattern <mcp-server-name>__<tool-name>.
Server name and tool name are separated by a double underscore __.
eg- If you register a MCP server github which provides a tool called git_commit, you can invoke it in MCPJungle using the name github__git_commit.
Your MCP client must also use this canonical name to call the tool via MCPJungle.
The config file format for registering a Streamable HTTP-based MCP server is:
{
"name": "<name of your mcp server>",
"transport": "streamable_http",
"description": "<description>",
"url": "<url of the mcp server>",
"bearer_token": "<optional bearer token for authentication>"
}Here's an example configuration file (let's call it filesystem.json) for a MCP server that uses the STDIO transport:
{
"name": "filesystem",
"transport": "stdio",
"description": "filesystem mcp server",
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "."]
}You can register this MCP server in MCPJungle by providing the configuration file:
mcpjungle register -c ./filesystem.jsonThe config file format for registering a STDIO-based MCP server is:
{
"name": "<name of your mcp server>",
"transport": "stdio",
"description": "<description>",
"command": "<command to run the mcp server, eg- 'npx', 'uvx'>",
"args": ["arguments", "to", "pass", "to", "the", "command"],
"env": {
"KEY": "value"
}
}Tip
If your STDIO server fails or throws errors for some reason, check the mcpjungle server's logs to view its stderr output.
Limitation 🚧
MCPJungle creates a new connection when a tool is called. This means a new sub-process for a STDIO mcp server is started for every tool call.
This has some performance overhead but ensures that there are no memory leaks.
But it also means that currently MCPJungle doesn't support stateful connections with your MCP server.
We want to hear your feedback to improve this mechanism, feel free to create an issue, start a discussion or just reach out on Discord.
You can remove a MCP server from mcpjungle.
mcpjungle deregister calculator
mcpjungle deregister filesystemOnce removed, this mcp server and its tools are no longer available to you or your MCP clients.
Assuming that MCPJungle is running on http://localhost:8080, use the following configurations to connect to it:
{
"mcpServers": {
"mcpjungle": {
"command": "npx",
"args": [
"mcp-remote",
"http://localhost:8080/mcp",
"--allow-http"
]
}
}
}{
"mcpServers": {
"mcpjungle": {
"url": "http://localhost:8080/mcp"
}
}
}You can enable or disable a specific tool or all the tools provided by an MCP Server.
If a tool is disabled, it is not available via the MCPJungle Proxy, so no MCP clients can view or call it.
# disable the `get-library-docs` tool provided by the `context7` MCP server
mcpjungle disable context7__get-library-docs
# re-enable the tool
mcpjungle enable context7__get-library-docs
# disable all tools provided by the `context7` MCP server
mcpjungle disable context7
# re-enable all tools of `context7`
mcpjungle enable context7A disabled tool is still accessible via mcpjungle's HTTP API, so humans can still manage it from the CLI (or any other HTTP client).
Note
When a new server is registered in MCPJungle, all its tools are enabled by default.
MCPJungle currently supports authentication if your Streamable HTTP MCP Server accepts static tokens for auth.
This is useful when using SaaS-provided MCP Servers like HuggingFace, Stripe, etc. which require your API token for authentication.
You can supply your token while registering the MCP server:
# If you specify the `--bearer-token` flag, MCPJungle will add the `Authorization: Bearer <token>` header to all requests made to this MCP server.
mcpjungle register --name huggingface --description "HuggingFace MCP Server" --url https://huggingface.co/mcp --bearer-token <your-hf-api-token>Or from your configuration file
{
"name": "huggingface",
"transport": "streamable_http",
"url": "https://huggingface.co/mcp",
"description": "hugging face mcp server",
"bearer_token": "<your-hf-api-token>"
}Support for Oauth flow is coming soon!
If you're running MCPJungle in your organisation, we recommend running the Server in the production mode:
# enable enterprise features by running in production mode
mcpjungle start --prod
# you can also specify the server mode as environment variable (valid values are `development` and `production`)
export SERVER_MODE=production
mcpjungle start
# this also works when running the server via docker-compose
SERVER_MODE=production docker-compose upBy default, mcpjungle server runs in development mode which is ideal for individuals running it locally.
In Production mode, the server enforces stricter security policies and will provide additional features like Authentication, ACLs, observability and more.
After starting the server in production mode, you must initialize it by running the following command on your client machine:
mcpjungle init-serverThis will create an admin user in the server and store its API access token in your home directory (~/.mcpjungle.conf).
You can then use the mcpjungle cli to make authenticated requests to the server.
In development mode, all MCP clients have full access to all the MCP servers registered in MCPJungle Proxy.
production mode lets you control which MCP clients can access which MCP servers.
Suppose you have registered 2 MCP servers calculator and github in MCPJungle in production mode.
By default, no MCP client can access these servers. You must create an MCP Client in mcpjungle and explicitly allow it to access the MCP servers.
# Create a new MCP client for your Cursor IDE to use. It can access the calculator and github MCP servers
mcpjungle create mcp-client cursor-local --allow "calculator, github"
MCP client 'cursor-local' created successfully!
Servers accessible: calculator,github
Access token: 1YHf2LwE1LXtp5lW_vM-gmdYHlPHdqwnILitBhXE4Aw
Send this token in the `Authorization: Bearer {token}` HTTP header.Mcpjungle creates an access token for your client.
Configure your client or agent to send this token in the Authorization header when making requests to the mcpjungle proxy.
For example, you can add the following configuration in Cursor to connect to MCPJungle:
{
"mcpServers": {
"mcpjungle": {
"url": "http://localhost:8080/mcp",
"headers": {
"Authorization": "Bearer 1YHf2LwE1LXtp5lW_vM-gmdYHlPHdqwnILitBhXE4Aw"
}
}
}
}A client that has access to a particular server this way can view and call all the tools provided by that server.
Note
If you don't specify the --allow flag, the MCP client will not be able to access any MCP servers.
We're not perfect yet, but we're working hard to get there!
When you call a tool in a Streamable HTTP server, mcpjungle creates a new connection to the server to serve the request.
When you call a tool in a STDIO server, mcpjungle creates a new connection and starts a new sub-process to run this server.
After servicing your request, it terminates this sub-process.
So a new stdio server process is started for every tool call.
This has some performance overhead but ensures that there are no memory leaks.
It also means that if you rely on stateful connections with your MCP server, mcpjungle can currently not provide that.
We plan on improving this mechanism in future releases and are open to ideas from the community!
This is a work in progress.
We're collecting more feedback on how people use OAuth with MCP servers, so feel free to start a Discussion or open an issue to share your use case.
If you're interested in contributing to MCPJungle, see Developer Docs.
