MCP Proxy Gateway

A context-aware MCP proxy that reduces token usage by exposing only 3 tools (mcp_search, mcp_call, mcp_schema) to LLMs instead of the full catalog.

Why This Exists

When you connect multiple MCP servers to an LLM, every tool from every server is listed in the LLM's context window. For a typical workspace with 50-100 tools across multiple MCP servers, that's thousands of tokens of schema documentation on every request.

MCP Proxy Gateway sits between your LLM and your MCP servers, offering:

• JIT tool loading — tools from upstream servers are discovered once at startup, then tools are called on-demand. Clients never see the full catalog.

• Intelligent search — fast lexical (BM25) search to find the right tool for a query. Tool tokens are pre-computed at startup for zero-overhead per-query scoring.

• Token savings — LLMs only see 3 tool schemas (search, call, schema) instead of 50+. Typical savings: 20-40% per turn for tool-heavy workflows.

• Zero native dependencies — pure JavaScript, no native modules, no model downloads, no supply chain risk from ML packages.

┌─────────────────────────────────────────────────────────────────┐
│                         Your LLM                                │
│   (sees only: mcp_search, mcp_call, mcp_schema)                │
└────────────────────┬────────────────────────────────────────────┘
                     │
         ┌───────────▼──────────────┐
         │   MCP Proxy Gateway      │
         │ ┌──────────────────────┐ │
         │ │ Tool Registry        │ │
         │ │ (BM25 lexical)       │ │
         │ └──────────────────────┘ │
         │ ┌──────────────────────┐ │
         │ │ Connector Manager    │ │
         │ │ (Idle timeout reap)  │ │
         │ └──────────────────────┘ │
         └────────────┬─────────────┘
                      │
        ┌─────────────┼─────────────┐
        │             │             │
        ▼             ▼             ▼
   ┌─────────┐  ┌─────────┐  ┌─────────┐
   │Google   │  │MailerLite│ │Your Svc │
   │Gmail    │  │ Campaigns│ │ Custom  │
   │Calendar │  │          │ │ Tools   │
   │Drive    │  │          │ │         │
   └─────────┘  └─────────┘  └─────────┘

Related MCP server: mcp-compressor

Prerequisites

• Node.js 18+ with npm or pnpm

• One or more MCP servers to proxy (stdio or HTTP)

Installation

From Source

git clone https://github.com/steveweltman/4q-tokenz.git
cd 4q-tokenz

pnpm install
pnpm build

# Install to ~/.local/bin and configure
./install.sh

As a Dependency

npm install -g 4q-tokenz

Getting Started: Google Workspace Example

Here's a concrete walkthrough to connect Google Workspace (Gmail, Calendar, Drive) to your LLM through the proxy:

Step 1: Choose or Build an MCP Server for Google

You need an MCP server that wraps Google APIs. Options:

• @antidrift/mcp-google (recommended) — A collection of MCP server implementations for Google Workspace (Gmail, Calendar, Drive, Docs, Sheets). Works out of the box with this proxy.

  npm install @antidrift/mcp-google
  # or
  npx @antidrift/mcp-google --help

• @modelcontextprotocol/server-gmail — Gmail-only, official MCP server

• Build your own — See the MCP spec to wrap your own APIs

Step 2: Set Up Google OAuth

1. Go to Google Cloud Console

2. Create a new project or select an existing one

3. Enable these APIs:

   • Gmail API

   • Google Calendar API

   • Google Drive API

4. Create an OAuth 2.0 credential (type: Desktop application)

5. Download the credential JSON

6. Run the Google MCP server once to generate token.json:

   GOOGLE_CREDENTIAL_FILE=~/Downloads/credentials.json \
   npx @antidrift/mcp-google

   This opens a browser for you to authorize. Once done, it saves token.json locally.

Step 3: Configure the Proxy

Create ~/.config/4q-tokens/config.json:

{
  "upstreams": [
    {
      "name": "google-workspace",
      "transport": "stdio",
      "command": "npx",
      "args": ["@antidrift/mcp-google"],
      "env": {
        "GOOGLE_TOKEN_FILE": "~/.local/share/google-mcp/token.json",
        "GOOGLE_CONNECTORS": "gmail,calendar,drive"
      }
    }
  ],
  "searchLimit": 5,
  "callItemLimit": 30,
  "maxTextLength": 800,
  "maxOutputTokens": 10000,
  "idleTimeoutMs": 600000
}

Step 4: Start the Proxy

mcp-proxy
# Or via systemd if installed:
systemctl --user start mcp-proxy

Step 5: Connect Your LLM

Configure your LLM to use http://127.0.0.1:9200/mcp as its MCP server. It will see:

• mcp_search — find tools by natural language

• mcp_call — invoke a tool

• mcp_schema — see tool details

Example query:

mcp_search("send an email")
# Returns: google_send_email (Gmail)

mcp_call(ref="google_send_email", args={"to": "user@example.com", "subject": "Hello", "body": "Test"})

Configuration

Quick Start with Environment Variables

export MCP_PROXY_UPSTREAMS='[
  {
    "name": "google",
    "transport": "stdio",
    "command": "node",
    "args": ["/path/to/google/server.mjs"],
    "env": {
      "GOOGLE_TOKEN_FILE": "token.json"
    }
  }
]'

export MCP_PROXY_SINGLETON_PORT=9200
export MCP_PROXY_DASHBOARD_PORT=9100

node dist/index.js

Config File (Recommended)

Create ~/.config/4q-tokens/config.json:

{
  "upstreams": [
    {
      "name": "google-workspace",
      "transport": "stdio",
      "command": "npx",
      "args": ["@antidrift/mcp-google"],
      "env": {
        "GOOGLE_TOKEN_FILE": "token.json",
        "GOOGLE_CONNECTORS": "gmail,calendar,drive"
      }
    },
    {
      "name": "external-api",
      "transport": "http",
      "url": "https://mcp.example.com/",
      "auth": {
        "apiKey": "MY_API_KEY_ENV_VAR"
      }
    }
  ],
  "searchLimit": 3,
  "callItemLimit": 20,
  "maxTextLength": 500,
  "maxOutputTokens": 8000,
  "idleTimeoutMs": 300000
}

Then run:

node dist/index.js

The proxy will load the config from ~/.config/4q-tokens/config.json if it exists, otherwise fall back to the MCP_PROXY_UPSTREAMS environment variable.

Configuration Reference

Upstream Server Config

{
  "name": "unique-id",
  "transport": "stdio" | "http",
  
  // For stdio transport:
  "command": "node",
  "args": ["path/to/server.mjs"],
  "cwd": "/working/dir",  // optional
  "env": { "KEY": "value" },  // optional
  
  // For http transport:
  "url": "https://example.com/mcp",
  "auth": {
    "apiKey": "ENV_VAR_NAME"  // reads from process.env[ENV_VAR_NAME]
  }
}

Proxy Options

Environment variable overrides:

export MCP_PROXY_SEARCH_LIMIT=5
export MCP_PROXY_CALL_ITEM_LIMIT=30
export MCP_PROXY_MAX_TEXT_LENGTH=800
export MCP_PROXY_MAX_OUTPUT_TOKENS=10000
export MCP_PROXY_IDLE_TIMEOUT_MS=600000

Running

Standalone (Stdio Transport)

node dist/index.js

The proxy connects via stdio to your LLM. Use it with Claude or other MCP clients.

As a Systemd User Service

The install script can set this up for you (see below), or manually:

1. Create ~/.config/systemd/user/mcp-proxy.service:

[Unit]
Description=MCP Proxy Gateway
After=network.target

[Service]
Type=simple
ExecStart=%h/.local/bin/mcp-proxy
Restart=on-failure
RestartSec=5s
Environment="PATH=%h/.local/bin:/usr/local/bin:/usr/bin"

[Install]
WantedBy=default.target

2. Enable and start:

systemctl --user daemon-reload
systemctl --user enable mcp-proxy
systemctl --user start mcp-proxy

3. View logs:

journalctl --user -u mcp-proxy -f

Prometheus Metrics (Port 9100)

The dashboard exposes a Prometheus-compatible /metrics endpoint:

curl http://localhost:9100/metrics

Metrics exposed:

Add a scrape job in your Prometheus/Alloy config:

scrape_configs:
  - job_name: mcp-proxy
    static_configs:
      - targets: ['localhost:9100']
    metrics_path: /metrics

HTTP Server (Port 9200)

The proxy always starts an HTTP transport on port 9200 by default. Set MCP_PROXY_SINGLETON_PORT to use a different port. This allows multiple clients to connect to a single proxy instance.

export MCP_PROXY_SINGLETON_PORT=9200
node dist/index.js &

# From another process:
curl -X POST http://127.0.0.1:9200/mcp -H "Content-Type: application/json" \
  -d '{"jsonrpc": "2.0", "method": "tools/call", "params": {...}}'

Troubleshooting

Upstream MCP Server Won't Connect

Check the server logs in the dashboard (port 9100 by default) or daemon logs:

journalctl --user -u mcp-proxy -e

The proxy logs:

• Tool discovery on startup

• Connection failures with error messages

• Upstream stderr (piped from stdio servers)

Proxy Crashes or Freezes

The proxy has comprehensive error handling to gracefully degrade on upstream failures:

• If an upstream tool call fails, the error is logged and returned to the client

• If all upstreams fail at discovery, startup fails with NO_UPSTREAMS

For unhandled errors, check:

journalctl --user -u mcp-proxy -n 50  # Last 50 lines

Tool Returns No Data

When a tool returns null or malformed data, the output shaper handles it gracefully:

• Null results return []

• Strings are wrapped as {value: string}

• CSV is auto-parsed if it looks like tabular data

• Raw binary content (images, files) is preserved via _rawContent

If a tool response looks truncated, retry with detail=true in mcp_call to disable output shaping:

mcp_call(ref="google_send_email", args={...}, detail=true)

Security & Networking

The proxy binds to 127.0.0.1 only for security — it's not accessible from the network by default. To access remotely:

• Same machine: Connect locally on 127.0.0.1:9200

• Remote access: Use Tailscale, SSH forwarding, or a VPN tunnel

  ssh -L 9200:127.0.0.1:9200 user@remote-host

• Systemd service: Access is local by default; no firewall rule needed

Known Limitations

• No automated tests — this is production-quality code used daily, but test suite is not included

Changelog

v1.19.0

• Add Prometheus /metrics endpoint on the dashboard port (9100 by default)

• Exposes: mcp_proxy_uptime_seconds, mcp_proxy_registered_tools, mcp_proxy_upstream_up, mcp_proxy_upstream_tools, mcp_proxy_calls_total, mcp_proxy_call_duration_ms_total, mcp_proxy_output_bytes_total

• Counters persist for the lifetime of the process; scrape with Prometheus/Alloy and visualize in Grafana

v1.18.0

• Drop @xenova/transformers entirely — eliminates the protobufjs/ONNX runtime supply chain

• Switch to pure lexical (BM25) search; tool tokens pre-computed at startup, zero per-query overhead

• No change to mcp_search quality for English-language tool catalogs

• Removes ~90MB model download on first run and all native module requirements

v1.17.1

• Pre-compute tool token sets at registry build time; lexical scoring now reads the cache instead of re-tokenizing on every query

• Add LRU cache for query embeddings (embeddings removed entirely in v1.18.0)

v1.17.0

• Switch to lighter English-optimized embedding model, reducing cold-start download by ~380MB (embeddings removed entirely in v1.18.0)

v1.16.0

• Update @modelcontextprotocol/sdk from ~1.22.0 to ^1.26.0 — resolves 3 high-severity CVEs: ReDoS, cross-client data leak, DNS rebinding

• Add pnpm override: protobufjs >=7.5.8 — resolves critical arbitrary code execution and multiple high CVEs in @xenova/transformers transitive dependency chain

• Add pnpm override: qs >=6.15.2 — resolves moderate DoS vulnerability in express transitive dependency

Attribution

MCP Proxy Gateway is a fork of @arvoretech/mcp-proxy, originally created by João Augusto and Árvore Educação.

Forked and extended with:

• Singleton mode for HTTP bridge

• Idle server reaping

• Comprehensive error handling

• Config file support

• Systemd integration

License

MIT. See LICENSE.