MCP Gateway

MCP Gateway is an advanced intermediary solution for Model Context Protocol (MCP) servers that centralizes and enhances your AI infrastructure.

MCP Gateway acts as an intermediary between LLMs and other MCP servers. It:

1. Reads server configurations from a mcp.json file located in your root directory.
2. Manages the lifecycle of configured MCP servers.
3. Intercepts requests and responses to sanitize sensitive information.
4. Provides a unified interface for discovering and interacting with all proxied MCPs.

Installation

Python (recommended)

Install the mcp-gateway package:

pip install mcp-gateway

--mcp-json-path - must lead to your mcp.json or claude_desktop_config.json
--plugin or -p - Specify the plugins to enable (can be used multiple times)

Usage

This example enables the basic guardrail for token masking and xetrack tracing plugin for filesystem MCP:

mcp-gateway --mcp-json-path ~/.cursor/mcp.json -p basic -p xetrack

You can add more MCPs that will be under the Gateway by putting the MCP server configuration under the "servers" key.

Cursor example:

{
  "mcpServers": {
      "mcp-gateway": {
          "command": "mcp-gateway",
          "args": [
              "--mcp-json-path",
              "~/.cursor/mcp.json",
              "--plugin",
              "basic",
              "--plugin",
              "xetrack"
          ],
          "servers": {
              "filesystem": {
                  "command": "npx",
                  "args": [
                      "-y",
                      "@modelcontextprotocol/server-filesystem",
                      "."
                  ]
              }
          }
      }
  }
}

Claude example:

Get <PYTHON_PATH>

which python

{
  "mcpServers": {
      "mcp-gateway": {
          "command": "<python path>",
          "args": [
            "-m",
            "mcp_gateway.server",
            "--mcp-json-path",
            "<path to claude_desktop_config>",
            "--plugin",
            "basic"
          ],
          "servers": {
              "filesystem": {
                  "command": "npx",
                  "args": [
                      "-y",
                      "@modelcontextprotocol/server-filesystem",
                      "."
                  ]
              }
          }
      }
  }
}

Docker

Build the image after clone this repo

docker build -t mcp/gateway .

{
  "mcpServers": {
      "mcp-gateway": {
          "command": "docker",
          "args": [
            "run",
            "--rm",
            "--mount", "type=bind,source=/Users/oro/Projects/playground/mcp-gateway,target=/app",
            "-i",
            "-v", "/Users/oro/.cursor/mcp.json:/config/mcp.json:ro",
            "-e", "LASSO_API_KEY=<LASSO_API_KEY>",
            "-v", "mcp-gateway-logs:/logs",
            "mcp/gateway:latest",
            "--mcp-json-path", "/config/mcp.json",
            "--plugin", "basic",
            "--plugin", "lasso"
          ],
          "servers": {
              "filesystem": {
                  "command": "npx",
                  "args": [
                      "-y",
                      "@modelcontextprotocol/server-filesystem",
                      "."
                  ]
              }
          }
      }
  }
}

In this example we use lasso and basic guardrail to show how we can pass enviroment varabile and arguments to the docker and how we can mount storage for the filesystem MCP. The Docker image can be built with optional dependencies required by certain plugins (e.g., presidio).
Use the INSTALL_EXTRAS build argument during the docker build command. Provide a comma-separated string of the desired extras: "presidio,xetrack"

Quickstart

Masking Sensitive Information

MCP Gateway will automatically mask the sensitive token in the response, preventing exposure of credentials while still providing the needed functionality.

1. Create a file with sensitive information:

   echo 'HF_TOKEN = "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"' > tokens.txt

2. When an agent requests to read this file through MCP Gateway:

   • Recommend to test with sonnet 3.7

   Use your mcp-gateway tools to read the ${pwd}/tokens.txt and return the HF_TOKEN
   

Output:

Usage

Start the MCP Gateway server with python_env config on this repository root:

mcp-gateway -p basic -p presidio

You can also debug the server using:

LOGLEVEL=DEBUG mcp-gateway --mcp-json-path ~/.cursor/mcp.json -p basic -p presidio

Tools

Here are the tools the MCP is using to create a proxy to the other MCP servers

• get_metadata - Provides information about all available proxied MCPs to help LLMs choose appropriate tools and resources
• run_tool - Executes capabilities from any proxied MCP after sanitizing the request and response

Plugins

Contribute

For more details on how the plugin system works, how to create your own plugins, or how to contribute, please see the Plugin System Documentation.

Guardrails

MCP Gateway supports various plugins to enhance security and functionality. Here's a summary of the built-in guardrail plugins:

Name	PII Masking	Token/Secret Masking	Custom Policy	Prompt Injection	Harmful Content
basic	❌	✅	❌	❌	❌
presidio	✅	❌	❌	❌	❌
lasso	✅	✅	✅	✅	✅

Note: To use the presidio plugin, you need to install it separately: pip install mcp-gateway[presidio].

Basic

mcp-gateway -p basic

Masking basic secerts

• azure client secret
• github tokens
• github oauth
• gcp api key
• aws access token
• jwt token
• gitlab session cookie
• huggingface access token
• microsoft teams webhook
• slack app token

Presidio

mcp-gateway -p presidio

Presidio is identification and anonymization package

• Credit Card
• IP
• Email
• Phone
• SSN
• Etc

Lasso

mcp-gateway -p lasso

Prerequisites

• Obtain a Lasso API key by signing up at Lasso Security.

To use Lasso Security's advanced AI safety guardrails, update your mcp.json configuration as follows:

1. Add the LASSO_API_KEY=<YOUR-API-KEY> to your environment variable or in the "env" section.
2. Insert other MCP servers configuration under key servers

Example:

{
  "mcpServers": {
      "mcp-gateway": {
          "command": "mcp-gateway",
          "args": [
              "--mcp-json-path",
              "~/.cursor/mcp.json",
              "-p",
              "lasso"
          ],
          "env": {
              "LASSO_API_KEY": "<lasso_token>"
          },
          "servers": {
              "filesystem": {
                  "command": "npx",
                  "args": [
                      "-y",
                      "@modelcontextprotocol/server-filesystem",
                      "."
                  ]
              }
          }
      }
  }
}

Features

🔍 Full visibility into MCP interactions with an Always-on monitoring.

🛡️ Mitigate GenAI-specific threats like prompt injection and sensitive data leakage in real-time with built-in protection that prioritizes security from deployment.

✨ Use flexible, natural language to craft security policies tailored to your business's unique needs.

⚡ Fast and easy installation for any deployment style. Monitor data flow to and from MCP in minutes with an intuitive, user-friendly dashboard.

The Lasso guardrail checks content through Lasso's API for security violations before processing requests and responses.

Read more on our website 👉 Lasso Security.

Tracing

Xetrack

xetrack is a lightweight package to track ml experiments, benchmarks, and monitor stractured data.

We can use it to debug and monitor tool calls with logs (loguru) or duckdb and sqlite. .

mcp-gateway -p xetrack

Prerequisites

pip install xetrack

Params

• XETRACK_DB_PATH - The sqlite db location.
  • All logs register in the events table.
  • If fancy objects return from the MCPs response, read about xetrack assets to retrive it.
• XETRACK_LOGS_PATH - The logs location
• FLATTEN_ARGUMENTS - Flatten the arguments, default true
• FLATTEN_RESPONSE - Flatten the response, default true
• It is recommend to to gitignore the logs location
• It is recommended to use DVC to manage the db file

Quickstart

{
    "mcpServers": {
        "mcp-gateway": {
            "command": "mcp-gateway",
            "args": [
                "--mcp-json-path",
                "~/.cursor/mcp.json",
                "-p",
                "xetrack"
            ],
            "env": {
                "XETRACK_DB_PATH": "tracing.db",
                "XETRACK_LOGS_PATH": "logs/"                
            },
            "servers": {
                "filesystem": {
                    "command": "npx",
                    "args": [
                        "-y",
                        "@modelcontextprotocol/server-filesystem",
                        "."
                    ]
                }
            }
        }
    }
}

Let's say you use the filesystem list_directory tool on path ".", you can find the call parameters under logs/<date>.log.

You can expolre using xetrack cli to query the db:

$ xt tail tracing.db --json --n=1
[
    {
        "timestamp": "2025-04-17 17:12:48.233126",
        "track_id": "mottled-stingray-0411",
        "meta": "f3be31e09667745f",
        "paths": null,
        "call_id": "deab617e-0a45-4950-9de9-3fb549810cf2",
        "capability_name": "list_directory",
        "content_type": "text",
        "content_annotations": "f3be31e09667745f",
        "response_type": "CallToolResult",
        "server_name": "filesystem",
        "capability_type": "tool",
        "isError": 0,
        "content_text": "[DIR] .cursor\n[DIR] .git\n[FILE] .gitignore\n[DIR] .pytest_cache\n[DIR] .venv\n[FILE] LICENSE\n[FILE] MANIFEST.in\n[FILE] README.md\n[DIR] docs\n[DIR] logs\n[DIR] mcp_gateway\n[FILE] pyproject.toml\n[FILE] requirements.txt\n[DIR] tests\n[DIR] tmp",
        "path": ".",
        "prompt": null
    }
]

With python

from xetrack import Reader

df = Reader("tracing.db").to_df()

With duckdb cli and ui

$ duckdb --ui
D INSTALL sqlite; LOAD sqlite; ATTACH 'tracing.db' (TYPE sqlite);
D SELECT server_name,capability_name,path,content_text FROM db.events LIMIT 1;

┌─────────────┬─────────────────┬─────────┬────────────────────────────────────┐
│ server_name │ capability_name │  path   │            content_text            │
│   varchar   │     varchar     │ varchar │              varchar               │
├─────────────┼─────────────────┼─────────┼────────────────────────────────────┤
│ filesystem  │ list_directory  │ .       │ [DIR] .cursor\n[DIR] .git\n[FILE…  │
└─────────────┴─────────────────┴─────────┴────────────────────────────────────┘

Of course you can use another MCP server to query the sqlite database 😊

How It Works

Your agent interacts directly with our MCP Gateway, which functions as a central router and management system. Each underlying MCP is individually wrapped and managed.

Key Features

Agnostic Guardrails

• Applies configurable security filters to both requests and responses.
• Prevents sensitive data exposure before information reaches your agent.
• Works consistently across all connected MCPs regardless of their native capabilities.

Unified Visibility

• Provides comprehensive dashboard for all your MCPs in a single interface.
• Includes intelligent risk assessment with MCP risk scoring.
• Delivers real-time status monitoring and performance metrics.

Advanced Tracking

• Maintains detailed logs of all requests and responses for each guardrail.
• Offers cost evaluation tools for MCPs requiring paid tokens.
• Provides usage analytics and pattern identification for optimization.
• Sanitizes sensitive information before forwarding requests to other MCPs.

License

MIT