OpenAI-Compatible MCP Gateway

Local Python MCP server that exposes fixed MCP tools for gpt, claude, and gemini, while still calling any OpenAI-style chat/completions backend underneath.

That means each target can be configured independently:

• its own API base URL
• its own API key or API key env var
• its own default model
• its own headers, query params, and endpoint paths

So if you want:

• gpt -> OpenAI directly
• claude -> OpenRouter
• gemini -> Electron Hub

you can do that cleanly with one section per target.

Tools

The server exposes:

• provider_status
• list_gpt_models
• list_claude_models
• list_gemini_models
• chat_gpt
• chat_claude
• chat_gemini
• simple_gpt_chat
• simple_claude_chat
• simple_gemini_chat

Configuration

By default the server reads config/providers.toml.

The repository only includes a safe example file at config/providers.example.toml. Create your local config/providers.toml from that example and keep your real keys there.

Override the config path with:

$env:OPENAI_COMPAT_MCP_CONFIG="C:\path\to\providers.toml"

The file is intentionally fixed-shape. No arbitrary provider registry.

[server]
name = "OpenAI-Compatible MCP Gateway"
timeout_seconds = 60

[gpt]
base_url = "https://api.openai.com/v1"
api_key_env = "OPENAI_API_KEY"
model = "gpt-4.1-mini"

[claude]
base_url = "https://api.anthropic.com/v1/openai"
api_key_env = "ANTHROPIC_API_KEY"
model = "claude-sonnet-4-5"

[gemini]
base_url = "https://generativelanguage.googleapis.com/v1beta/openai"
api_key_env = "GEMINI_API_KEY"
model = "gemini-2.5-flash"

Bootstrap your local config with:

Copy-Item config\\providers.example.toml config\\providers.toml

Each of gpt, claude, and gemini supports:

• base_url
• model
• api_key_env
• api_key
• chat_completions_path
• models_path
• api_key_header
• api_key_prefix
• api_key_query_name
• headers
• query
• default_body
• timeout_seconds
• enabled

Example alternate routing

If you want all three targets to go through OpenRouter or another OpenAI-compatible hub, keep the sections separate and just point them to different models:

[gpt]
base_url = "https://openrouter.ai/api/v1"
api_key_env = "OPENROUTER_API_KEY"
model = "openai/gpt-4.1-mini"
headers = { "HTTP-Referer" = "https://example.com", "X-Title" = "Local MCP Gateway" }

[claude]
base_url = "https://openrouter.ai/api/v1"
api_key_env = "OPENROUTER_API_KEY"
model = "anthropic/claude-sonnet-4"
headers = { "HTTP-Referer" = "https://example.com", "X-Title" = "Local MCP Gateway" }

[gemini]
base_url = "https://openrouter.ai/api/v1"
api_key_env = "OPENROUTER_API_KEY"
model = "google/gemini-2.5-flash"
headers = { "HTTP-Referer" = "https://example.com", "X-Title" = "Local MCP Gateway" }

Install

python -m venv .venv
.venv\Scripts\Activate.ps1
pip install -e .[dev]

Run

For stdio MCP:

openai-compat-mcp

For streamable HTTP:

$env:OPENAI_COMPAT_MCP_TRANSPORT="streamable-http"
openai-compat-mcp

Optional Remote Bearer Auth

If you expose the server over HTTP, you can require an app-level bearer token.

Set:

$env:OPENAI_COMPAT_MCP_BEARER_TOKEN="replace-this-with-a-long-random-token"

Optional but recommended for remote/public-facing setups:

$env:OPENAI_COMPAT_MCP_PUBLIC_BASE_URL="https://your-domain.example.com"

Behavior:

• stdio mode is unaffected
• HTTP MCP requests must send Authorization: Bearer <your-token>
• provider_status reports whether remote bearer auth is enabled

Example MCP client config

{
  "mcpServers": {
    "openai-compat-gateway": {
      "command": "C:\\Users\\anuji\\Documents\\codex\\.venv\\Scripts\\openai-compat-mcp.exe",
      "env": {
        "OPENAI_COMPAT_MCP_CONFIG": "C:\\Users\\anuji\\Documents\\codex\\config\\providers.toml",
        "OPENAI_API_KEY": "sk-...",
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "GEMINI_API_KEY": "..."
      }
    }
  }
}

Notes

• The gateway uses direct HTTP requests, not vendor SDKs.
• Requests are non-streaming chat/completions.
• list_*_models depends on the configured backend exposing GET /models.