MCPJungle

<h1 align="center"> :deciduous_tree: MCPJungle :deciduous_tree: </h1> <p align="center"> Self-hosted MCP Gateway for your private AI agents </p> <p align="center"> <a href="https://discord.gg/CapV4Z3krk" style="text-decoration: none;"> <img src="https://img.shields.io/badge/Discord-MCPJungle-5865F2?style=flat-square&logo=discord&logoColor=white" alt="Discord" style="max-width: 100%;"> </a> </p> MCPJungle is a single source-of-truth registry for all [Model Context Protocol](https://modelcontextprotocol.io/introduction) 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.  <p align="center">MCPJungle is the only MCP Server your AI agents need to connect to!</p> # Who should use MCPJungle? 1. **Developers** using MCP Clients like Claude & Cursor that need to access MCP servers for tool-calling 2. **Developers** building production-grade AI Agents that need to access MCP servers with built-in security, privacy and Access Control. 3. **Organisations** wanting to view & manage all MCP client-server interactions from a central place. Hosted in their own datacenter 🔒 # 📋 Table of Contents - [Quick Start guide](#quickstart-guide) - [Installation](#installation) - [Usage](#usage) - [Server](#server) - [Running mcpjungle server inside Docker](#running-inside-docker) - [Running mcpjungle server directly on the host machine](#running-directly-on-host) - [Client](#client) - [Adding Streamable HTTP-based MCP servers](#registering-streamable-http-based-servers) - [Adding STDIO-based MCP servers](#registering-stdio-based-servers) - [Removing MCP servers](#deregistering-mcp-servers) - [Connect to mcpjungle from Claude](#claude) - [Connect to mcpjungle from Cursor](#cursor) - [Enabling/Disabling Tools globally](#enablingdisabling-tools) - [Tool Groups](#tool-groups) - [Authentication](#authentication) - [Enterprise features](#enterprise-features-) - [Access Control](#access-control) - [OpenTelemetry](#opentelemetry) - [Limitations](#current-limitations-) - [Contributing](#contributing-) # Quickstart guide This quickstart guide will show you how to: 1. Start the MCPJungle server locally using `docker compose` 2. Register a simple MCP server in mcpjungle 3. Connect your Claude to mcpjungle to access your MCP tools ## Start the server ```bash curl -O https://raw.githubusercontent.com/mcpjungle/MCPJungle/refs/heads/main/docker-compose.yaml docker compose up -d ``` ## Register MCP servers Download the `mcpjungle` CLI on your local machine either using brew or directly from the [Releases Page](https://github.com/mcpjungle/MCPJungle/releases). ```bash brew install mcpjungle/mcpjungle/mcpjungle ``` The CLI lets you manage everything in mcpjungle. Next, lets add an MCP server to mcpjungle using the CLI. For this example, we'll use [context7](https://context7.com/). ```bash mcpjungle register --name context7 --url https://mcp.context7.com/mcp ``` ## Connect to mcpjungle Use the following configuration for your Claude MCP servers config: ```json { "mcpServers": { "mcpjungle": { "command": "npx", "args": [ "mcp-remote", "http://localhost:8080/mcp", "--allow-http" ] } } } ``` Once mcpjungle is added as an MCP to your Claude, try asking it the following: ```text 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. <p align="center"> <img src="./assets/quickstart-claude-call-tool.png" alt="claude calls context7 tool via mcpjungle" height="400"> </p> Congratulations! 🎉 You have successfully registered a remote MCP server in MCPJungle and called one of its tools via Claude You can now proceed to play around with the mcpjungle and explore the documentation & CLI for more details. # Installation > [!WARNING] > MCPJungle is **BETA** software. > > We're actively working to make it production-ready. > You can provide your feedback by [starting a discussion](https://github.com/mcpjungle/MCPJungle/discussions) in this repository. MCPJungle is shipped as a stand-alone binary. You can either download it from the [Releases](https://github.com/mcpjungle/MCPJungle/releases) Page or use [Homebrew](https://brew.sh/) to install it: ```bash brew install mcpjungle/mcpjungle/mcpjungle ``` Verify your installation by running ```bash mcpjungle version ``` > [!IMPORTANT] > On MacOS, you will have to use homebrew because the compiled binary is not [Notarized](https://developer.apple.com/documentation/security/notarizing-macos-software-before-distribution) yet. MCPJungle provides a Docker image which is useful for running the registry server (more about it later). ```bash docker pull mcpjungle/mcpjungle ``` # Usage MCPJungle has a Client-Server architecture and the binary lets you run both the Server and the Client. ## Server 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. ### Running inside Docker For running the MCPJungle server locally, docker compose is the recommended way: ```shell # docker-compose.yaml is optimized for individuals running mcpjungle on their local machines for personal use. # mcpjungle will run in `development` mode by default. curl -O https://raw.githubusercontent.com/mcpjungle/MCPJungle/refs/heads/main/docker-compose.yaml docker compose up -d # docker-compose.prod.yaml is optimized for orgs deploying mcpjungle on a remote server for multiple users. # mcpjungle will run in `production` mode by default, which enables enterprise features. curl -O https://raw.githubusercontent.com/mcpjungle/MCPJungle/refs/heads/main/docker-compose.prod.yaml docker compose -f docker-compose.prod.yaml up -d ``` This will start the MCPJungle server along with a persistent Postgres database container. You can quickly verify that the server is running: ```bash curl http://localhost:8080/health ``` If you plan on registering stdio-based MCP servers that rely on `npx` or `uvx`, use mcpjungle's `stdio` tagged docker image instead. ```bash MCPJUNGLE_IMAGE_TAG=latest-stdio docker compose up -d ``` > [!NOTE] > If you're using `docker-compose.yaml`, this is already the default image tag. > You only need to specify the stdio image tag if you're using `docker-compose.prod.yaml`. This 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](https://hub.docker.com/r/mcpjungle/mcpjungle) 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](#database) section below). You can see the definitions of the [standard Docker image](./Dockerfile) and the [stdio Docker image](./stdio.Dockerfile). ### Running directly on host You can also run the server directly on your host machine using the binary: ```bash mcpjungle start ``` This starts the main registry server and MCP gateway, accessible on port `8080` by default. ### Database 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: ```bash export DATABASE_URL=postgres://admin:root@localhost:5432/mcpjungle_db #run as container docker run mcpjungle/mcpjungle:latest # or run directly mcpjungle start ``` ## Client Once the server is up, you can use the mcpjungle CLI to interact with it. MCPJungle currently supports MCP servers using [stdio](https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#stdio) and [Streamable HTTP](https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#streamable-http) Transports. Let's see how to register them in mcpjungle. ### Registering streamable HTTP-based servers 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: ```bash mcpjungle register --name calculator --description "Provides some basic math tools" --url http://127.0.0.1:8000/mcp ``` If 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. ```bash mcpjungle register --name calculator --description "Provides some basic math tools" --url http://host.docker.internal:8000/mcp ``` The registry will now start tracking this MCP server and load its tools.  You can also provide a configuration file to register the MCP server: ```bash 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.json ``` All tools provided by this server are now accessible via MCPJungle: ```bash 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: ```json { "name": "<name of your mcp server>", "transport": "streamable_http", "description": "<description>", "url": "<url of the mcp server>", "bearer_token": "<optional bearer token for authentication>" } ``` ### Registering STDIO-based servers Here's an example configuration file (let's call it `filesystem.json`) for a MCP server that uses the STDIO transport: ```json { "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: ```bash mcpjungle register -c ./filesystem.json ``` The config file format for registering a STDIO-based MCP server is: ```json { "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" } } ``` You can also watch a quick video on [How to register a STDIO-based MCP server](https://youtu.be/YqHiuexR5fw). > [!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. ### Deregistering MCP servers You can remove a MCP server from mcpjungle. ```bash mcpjungle deregister calculator mcpjungle deregister filesystem ``` Once removed, this mcp server and its tools are no longer available to you or your MCP clients. ## Integration with other MCP Clients Assuming that MCPJungle is running on `http://localhost:8080`, use the following configurations to connect to it: ### Claude ```json { "mcpServers": { "mcpjungle": { "command": "npx", "args": [ "mcp-remote", "http://localhost:8080/mcp", "--allow-http" ] } } } ``` ### Cursor ```json { "mcpServers": { "mcpjungle": { "url": "http://localhost:8080/mcp" } } } ``` You can watch a quick video on [How to connect Cursor to MCPJungle](https://youtu.be/SaUqj-eLPnw). ## Enabling/Disabling Tools 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. ```bash # 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 context7 ``` A 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. ## Tool Groups As you add more MCP servers to MCPJungle, the number of tools available through the Gateway can grow significantly. If your MCP client is exposed to hundreds of tools through the gateway MCP, its performance may degrade. MCPJungle allows you to **expose only a subset of all available tools to your MCP clients using Tool Groups**. You can create a new group and only include specific tools that you wish to expose. Once a group is created, mcpjungle returns a unique endpoint for it. You can then configure your MCP client to use this group-specific endpoint instead of the main gateway endpoint. ### Creating a Tool Group You can create a new tool group by providing a JSON configuration file to the `create group` command. You must specify a unique `name` for the group and a list of `included_tools` that you want to expose via its MCP proxy. Here is an example of a tool group configuration file (`claude-tools-group.json`): ```json { "name": "claude-tools", "description": "This group only contains tools for Claude Desktop to use", "included_tools": [ "filesystem__read_file", "deepwiki__read_wiki_contents", "time__get_current_time" ] } ``` Instead of exposing 20 tools across all MCP servers, this group only exposes 3 handpicked ones. You can create this group in mcpjungle: ```bash $ mcpjungle create group -c ./claude-tools-group.json Tool Group claude-tools created successfully It is now accessible at the following streamable http endpoint: http://127.0.0.1:8080/v0/groups/claude-tools/mcp ``` You can then configure Claude (or any other MCP client) to use this group-specific endpoint to access the MCP server. The client will then ONLY see and be able to use these 3 tools and will not be aware of any other tools registered in MCPJungle. > [!TIP] > You can run `mcpjungle list tools` to view all available tools and pick the ones you want to include in your group. You can also watch a [Video on using Tool Groups](https://youtu.be/A21rfGgo38A). ### Managing tool groups You can currently perform operations like listing all groups, viewing details of a specific group and deleting a group. ```bash # list all tool groups mcpjungle list groups # view details of a specific group mcpjungle get group claude-tools # delete a group mcpjungle delete group claude-tools ``` > [!NOTE] > If a tool is included in a group but is later disabled globally or deleted, then it will not be available via the group's MCP endpoint. > > But if the tool is re-enabled or added again later, it will automatically become available in the group again. **Limitations** 🚧 1. Currently, you cannot update an existing tool group. You must delete the group and create a new one with the modified configuration file. 2. In `production` mode, currently only an admin can create a Tool Group. We're working on allowing standard Users to create their own groups as well. ## Authentication 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: ```bash # 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 ```bash { "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! ## Enterprise Features 🔒 If you're running MCPJungle in your organisation, we recommend running the Server in the `production` mode: ```bash # 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 # Or use the production docker compose file as described above docker compose -f docker-compose.prod.yaml up -d ``` By 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: ```bash mcpjungle init-server ``` This 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. ### Access Control 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.** ```bash # 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: ```json { "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. ### OpenTelemetry MCPJungle supports Prometheus-compatible OpenTelemetry Metrics for observability. - In `production` mode, OpenTelemetry is enabled by default. - In `development` mode, telemetry is disabled by default. You can enable it by setting the `OTEL_ENABLED` environment variable to `true` before starting the server: ```bash # enable OpenTelemetry metrics export OTEL_ENABLED=true # optionally, set additional attributes to be added to all metrics export OTEL_RESOURCE_ATTRIBUTES=deployment.environment.name=production # start the server mcpjungle start ``` Once the mcpjungle server is started, metrics are available at the `/metrics` endpoint. # Current limitations 🚧 We're not perfect yet, but we're working hard to get there! ### 1. MCPJungle doesn't maintain any long-running connections to the registered MCP Servers 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! ### 2. MCPJungle does not support OAuth flow for authentication. 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. # Contributing 💻 We welcome contributions from the community! - **For contribution guidelines and standards**, see [CONTRIBUTION.md](./CONTRIBUTION.md) - **For development setup and technical details**, see [DEVELOPMENT.md](./DEVELOPMENT.md) Join our [Discord community](https://discord.gg/CapV4Z3krk) to connect with other contributors and maintainers.