MCP Overview

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MCP Overview

LiteLLM Proxy provides an MCP Gateway that allows you to use a fixed endpoint for all MCP tools and control MCP access by Key, Team.

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Overview

:::caution MCP protocol update Starting in LiteLLM v1.80.18, the LiteLLM MCP protocol version is 2025-11-25. LiteLLM namespaces multiple MCP servers by prefixing each tool name with its MCP server name, so newly created servers now must use names that comply with SEP-986—noncompliant names cannot be added anymore. Existing servers that still violate SEP-986 only emit warnings today, but future MCP-side rollouts may block those names entirely, so we recommend updating any legacy server names proactively before MCP enforcement makes them unusable. :::

Adding your MCP

Prerequisites

To store MCP servers in the database, you need to enable database storage:

Environment Variable:

export STORE_MODEL_IN_DB=True

OR in config.yaml:

general_settings:
  store_model_in_db: true

Fine-grained Database Storage Control

By default, when store_model_in_db is true, all object types (models, MCPs, guardrails, vector stores, etc.) are stored in the database. If you want to store only specific object types, use the supported_db_objects setting.

Example: Store only MCP servers in the database

general_settings:
  store_model_in_db: true
  supported_db_objects: ["mcp"]  # Only store MCP servers in DB

model_list:
  - model_name: gpt-4o
    litellm_params:
      model: openai/gpt-4o
      api_key: sk-xxxxxxx

See all available object types: Config Settings - supported_db_objects

If supported_db_objects is not set, all object types are loaded from the database (default behavior).

For diagnosing connectivity problems after setup, see the MCP Troubleshooting Guide.

On the LiteLLM UI, Navigate to "MCP Servers" and click "Add New MCP Server".

On this form, you should enter your MCP Server URL and the transport you want to use.

LiteLLM supports the following MCP transports:

• Streamable HTTP
• SSE (Server-Sent Events)
• Standard Input/Output (stdio)

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Add HTTP MCP Server

This video walks through adding and using an HTTP MCP server on LiteLLM UI and using it in Cursor IDE.

Add SSE MCP Server

This video walks through adding and using an SSE MCP server on LiteLLM UI and using it in Cursor IDE.

Add STDIO MCP Server

For stdio MCP servers, select "Standard Input/Output (stdio)" as the transport type and provide the stdio configuration in JSON format:

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OAuth Configuration & Overrides

LiteLLM attempts OAuth 2.0 Authorization Server Discovery by default. When you create an MCP server in the UI and set Authentication: OAuth, LiteLLM will locate the provider metadata, dynamically register a client, and perform PKCE-based authorization without you providing any additional details.

Customize the OAuth flow when needed:

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• Provide explicit client credentials – If the MCP provider does not offer dynamic client registration or you prefer to manage the client yourself, fill in client_id, client_secret, and the desired scopes.
• Override discovery URLs – In some environments, LiteLLM might not be able to reach the provider's metadata endpoints. Use the optional authorization_url, token_url, and registration_url fields to point LiteLLM directly to the correct endpoints.

Static Headers

Sometimes your MCP server needs specific headers on every request. Maybe it's an API key, maybe it's a custom header the server expects. Instead of configuring auth, you can just set them directly.

<Image img={require('../img/static_headers.png')} style={{width: '80%', display: 'block', margin: '0'}} />

These headers get sent with every request to the server. That's it.

When to use this:

• Your server needs custom headers that don't fit the standard auth patterns
• You want full control over exactly what headers are sent
• You're debugging and need to quickly add headers without changing auth configuration

Add your MCP servers directly in your config.yaml file:

model_list:
  - model_name: gpt-4o
    litellm_params:
      model: openai/gpt-4o
      api_key: sk-xxxxxxx

litellm_settings:
  # MCP Aliases - Map aliases to server names for easier tool access
  mcp_aliases:
    "github": "github_mcp_server"
    "zapier": "zapier_mcp_server"
    "deepwiki": "deepwiki_mcp_server"

mcp_servers:
  # HTTP Streamable Server
  deepwiki_mcp:
    url: "https://mcp.deepwiki.com/mcp"
  # SSE Server
  zapier_mcp:
    url: "https://actions.zapier.com/mcp/sk-akxxxxx/sse"
  
  # Standard Input/Output (stdio) Server - CircleCI Example
  circleci_mcp:
    transport: "stdio"
    command: "npx"
    args: ["-y", "@circleci/mcp-server-circleci"]
    env:
      CIRCLECI_TOKEN: "your-circleci-token"
      CIRCLECI_BASE_URL: "https://circleci.com"
  
  # Full configuration with all optional fields
  my_http_server:
    url: "https://my-mcp-server.com/mcp"
    transport: "http"
    description: "My custom MCP server"
    auth_type: "api_key"
    auth_value: "abc123"

Configuration Options:

• Server Name: Use any descriptive name for your MCP server (e.g., zapier_mcp, deepwiki_mcp, circleci_mcp)

• Alias: This name will be prefilled with the server name with "_" replacing spaces, else edit it to be the prefix in tool names

• URL: The endpoint URL for your MCP server (required for HTTP/SSE transports)

• Transport: Optional transport type (defaults to sse)

  • sse - SSE (Server-Sent Events) transport
  • http - Streamable HTTP transport
  • stdio - Standard Input/Output transport

• Command: The command to execute for stdio transport (required for stdio)

• allow_all_keys: Set to true to make the server available to every LiteLLM API key, even if the key/team doesn't list the server in its MCP permissions.

• Args: Array of arguments to pass to the command (optional for stdio)

• Env: Environment variables to set for the stdio process (optional for stdio)

• Description: Optional description for the server

• Auth Type: Optional authentication type. Supported values:

  Value	Header sent
  api_key	X-API-Key: <auth_value>
  bearer_token	Authorization: Bearer <auth_value>
  basic	Authorization: Basic <auth_value>
  authorization	Authorization: <auth_value>

• Extra Headers: Optional list of additional header names that should be forwarded from client to the MCP server

• Static Headers: Optional map of header key/value pairs to include every request to the MCP server.

• Spec Version: Optional MCP specification version (defaults to 2025-06-18)

Examples for each auth type:

mcp_servers:
  api_key_example:
    url: "https://my-mcp-server.com/mcp"
    auth_type: "api_key"
    auth_value: "abc123"        # headers={"X-API-Key": "abc123"}

  # NEW – OAuth 2.0 Client Credentials (v1.77.5)
  oauth2_example:
    url: "https://my-mcp-server.com/mcp"
    auth_type: "oauth2"         # 👈 KEY CHANGE
    authorization_url: "https://my-mcp-server.com/oauth/authorize" # optional override
    token_url: "https://my-mcp-server.com/oauth/token"             # optional override
    registration_url: "https://my-mcp-server.com/oauth/register"   # optional override
    client_id: os.environ/OAUTH_CLIENT_ID
    client_secret: os.environ/OAUTH_CLIENT_SECRET
    scopes: ["tool.read", "tool.write"] # optional override

  bearer_example:
    url: "https://my-mcp-server.com/mcp"
    auth_type: "bearer_token"
    auth_value: "abc123"        # headers={"Authorization": "Bearer abc123"}

  basic_example:
    url: "https://my-mcp-server.com/mcp"
    auth_type: "basic"
    auth_value: "dXNlcjpwYXNz"  # headers={"Authorization": "Basic dXNlcjpwYXNz"}

  custom_auth_example:
    url: "https://my-mcp-server.com/mcp"
    auth_type: "authorization"
    auth_value: "Token example123"  # headers={"Authorization": "Token example123"}

  # Example with extra headers forwarding
  github_mcp:
    url: "https://api.githubcopilot.com/mcp"
    auth_type: "bearer_token"
    auth_value: "ghp_example_token"
    extra_headers: ["custom_key", "x-custom-header"]  # These headers will be forwarded from client

  # Example with static headers
  my_mcp_server:
    url: "https://my-mcp-server.com/mcp"
    static_headers: # These headers will be requested to the MCP server
      X-API-Key: "abc123"
      X-Custom-Header: "some-value"

MCP Walkthroughs

• Strands (STDIO) – watch tutorial

Add it from the UI

{
  "mcpServers": {
    "strands-agents": {
      "command": "uvx",
      "args": ["strands-agents-mcp-server"],
      "env": {
        "FASTMCP_LOG_LEVEL": "INFO"
      },
      "disabled": false,
      "autoApprove": ["search_docs", "fetch_doc"]
    }
  }
}

config.yml

mcp_servers:
  strands_mcp:
    transport: "stdio"
    command: "uvx"
    args: ["strands-agents-mcp-server"]
    env:
      FASTMCP_LOG_LEVEL: "INFO"

MCP Aliases

You can define aliases for your MCP servers in the litellm_settings section. This allows you to:

1. Map friendly names to server names: Use shorter, more memorable aliases
2. Override server aliases: If a server doesn't have an alias defined, the system will use the first matching alias from mcp_aliases
3. Ensure uniqueness: Only the first alias for each server is used, preventing conflicts

Example:

litellm_settings:
  mcp_aliases:
    "github": "github_mcp_server"      # Maps "github" alias to "github_mcp_server"
    "zapier": "zapier_mcp_server"      # Maps "zapier" alias to "zapier_mcp_server"
    "docs": "deepwiki_mcp_server"      # Maps "docs" alias to "deepwiki_mcp_server"
    "github_alt": "github_mcp_server"  # This will be ignored since "github" already maps to this server

Benefits:

• Simplified tool access: Use github_create_issue instead of github_mcp_server_create_issue
• Consistent naming: Standardize alias patterns across your organization
• Easy migration: Change server names without breaking existing tool references

Converting OpenAPI Specs to MCP Servers

LiteLLM can automatically convert OpenAPI specifications into MCP servers, allowing you to expose any REST API as MCP tools. This is useful when you have existing APIs with OpenAPI/Swagger documentation and want to make them available as MCP tools.

Benefits:

• Rapid Integration: Convert existing APIs to MCP tools without writing custom MCP server code
• Automatic Tool Generation: LiteLLM automatically generates MCP tools from your OpenAPI spec
• Unified Interface: Use the same MCP interface for both native MCP servers and OpenAPI-based APIs
• Easy Testing: Test and iterate on API integrations quickly

Configuration:

Add your OpenAPI-based MCP server to your config.yaml:

model_list:
  - model_name: gpt-4o
    litellm_params:
      model: openai/gpt-4o
      api_key: sk-xxxxxxx

mcp_servers:
  # OpenAPI Spec Example - Petstore API
  petstore_mcp:
    url: "https://petstore.swagger.io/v2"
    spec_path: "/path/to/openapi.json"
    auth_type: "none"
  
  # OpenAPI Spec with API Key Authentication
  my_api_mcp:
    url: "http://0.0.0.0:8090"
    spec_path: "/path/to/openapi.json"
    auth_type: "api_key"
    auth_value: "your-api-key-here"
  
  # OpenAPI Spec with Bearer Token
  secured_api_mcp:
    url: "https://api.example.com"
    spec_path: "/path/to/openapi.json" 
    auth_type: "bearer_token"
    auth_value: "your-bearer-token"

Configuration Parameters:

Usage Example

Once configured, you can use the OpenAPI-based MCP server just like any other MCP server:

from fastmcp import Client
import asyncio

# Standard MCP configuration
config = {
    "mcpServers": {
        "petstore": {
            "url": "http://localhost:4000/petstore_mcp/mcp",
            "headers": {
                "x-litellm-api-key": "Bearer sk-1234"
            }
        }
    }
}

# Create a client that connects to the server
client = Client(config)

async def main():
    async with client:
        # List available tools generated from OpenAPI spec
        tools = await client.list_tools()
        print(f"Available tools: {[tool.name for tool in tools]}")

        # Example: Get a pet by ID (from Petstore API)
        response = await client.call_tool(
            name="getpetbyid", 
            arguments={"petId": "1"}
        )
        print(f"Response:\n{response}\n")

        # Example: Find pets by status
        response = await client.call_tool(
            name="findpetsbystatus", 
            arguments={"status": "available"}
        )
        print(f"Response:\n{response}\n")

if __name__ == "__main__":
    asyncio.run(main())

{
  "mcpServers": {
    "Petstore": {
      "url": "http://localhost:4000/petstore_mcp/mcp",
      "headers": {
        "x-litellm-api-key": "Bearer $LITELLM_API_KEY"
      }
    }
  }
}

curl --location 'https://api.openai.com/v1/responses' \
--header 'Content-Type: application/json' \
--header "Authorization: Bearer $OPENAI_API_KEY" \
--data '{
    "model": "gpt-4o",
    "tools": [
        {
            "type": "mcp",
            "server_label": "petstore",
            "server_url": "http://localhost:4000/petstore_mcp/mcp",
            "require_approval": "never",
            "headers": {
                "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY"
            }
        }
    ],
    "input": "Find all available pets in the petstore",
    "tool_choice": "required"
}'

How It Works

1. Spec Loading: LiteLLM loads your OpenAPI specification from the provided spec_path
2. Tool Generation: Each API endpoint in the spec becomes an MCP tool
3. Parameter Mapping: OpenAPI parameters are automatically mapped to MCP tool parameters
4. Request Handling: When a tool is called, LiteLLM converts the MCP request to the appropriate HTTP request
5. Response Translation: API responses are converted back to MCP format

OpenAPI Spec Requirements

Your OpenAPI specification should follow standard OpenAPI/Swagger conventions:

• Supported versions: OpenAPI 3.0.x, OpenAPI 3.1.x, Swagger 2.0
• Required fields: paths, info sections should be properly defined
• Operation IDs: Each operation should have a unique operationId (this becomes the tool name)
• Parameters: Request parameters should be properly documented with types and descriptions

MCP OAuth

LiteLLM supports OAuth 2.0 for MCP servers -- both interactive (PKCE) flows for user-facing clients and machine-to-machine (M2M) client_credentials for backend services.

See the MCP OAuth guide for setup instructions, sequence diagrams, and a test server.

LiteLLM v 1.77.6 added support for OAuth 2.0 Client Credentials for MCP servers.

You can configure this either in config.yaml or directly from the LiteLLM UI (MCP Servers → Authentication → OAuth).

mcp_servers:
  github_mcp:
    url: "https://api.githubcopilot.com/mcp"
    auth_type: oauth2
    client_id: os.environ/GITHUB_OAUTH_CLIENT_ID
    client_secret: os.environ/GITHUB_OAUTH_CLIENT_SECRET

See Claude Code Tutorial

How It Works

sequenceDiagram
    participant Browser as User-Agent (Browser)
    participant Client as Client
    participant LiteLLM as LiteLLM Proxy
    participant MCP as MCP Server (Resource Server)
    participant Auth as Authorization Server

    Note over Client,LiteLLM: Step 1 – Resource discovery
    Client->>LiteLLM: GET /.well-known/oauth-protected-resource/{mcp_server_name}/mcp
    LiteLLM->>Client: Return resource metadata

    Note over Client,LiteLLM: Step 2 – Authorization server discovery
    Client->>LiteLLM: GET /.well-known/oauth-authorization-server/{mcp_server_name}
    LiteLLM->>Client: Return authorization server metadata

    Note over Client,Auth: Step 3 – Dynamic client registration
    Client->>LiteLLM: POST /{mcp_server_name}/register
    LiteLLM->>Auth: Forward registration request
    Auth->>LiteLLM: Issue client credentials
    LiteLLM->>Client: Return client credentials

    Note over Client,Browser: Step 4 – User authorization (PKCE)
    Client->>Browser: Open authorization URL + code_challenge + resource
    Browser->>Auth: Authorization request
    Note over Auth: User authorizes
    Auth->>Browser: Redirect with authorization code
    Browser->>LiteLLM: Callback to LiteLLM with code
    LiteLLM->>Browser: Redirect back with authorization code
    Browser->>Client: Callback with authorization code

    Note over Client,Auth: Step 5 – Token exchange
    Client->>LiteLLM: Token request + code_verifier + resource
    LiteLLM->>Auth: Forward token request
    Auth->>LiteLLM: Access (and refresh) token
    LiteLLM->>Client: Return tokens

    Note over Client,MCP: Step 6 – Authenticated MCP call
    Client->>LiteLLM: MCP request with access token + LiteLLM API key
    LiteLLM->>MCP: MCP request with Bearer token
    MCP-->>LiteLLM: MCP response
    LiteLLM-->>Client: Return MCP response

Participants

• Client – The MCP-capable AI agent (e.g., Claude Code, Cursor, or another IDE/agent) that initiates OAuth discovery, authorization, and tool invocations on behalf of the user.
• LiteLLM Proxy – Mediates all OAuth discovery, registration, token exchange, and MCP traffic while protecting stored credentials.
• Authorization Server – Issues OAuth 2.0 tokens via dynamic client registration, PKCE authorization, and token endpoints.
• MCP Server (Resource Server) – The protected MCP endpoint that receives LiteLLM’s authenticated JSON-RPC requests.
• User-Agent (Browser) – Temporarily involved so the end user can grant consent during the authorization step.

Flow Steps

1. Resource Discovery: The client fetches MCP resource metadata from LiteLLM’s .well-known/oauth-protected-resource endpoint to understand scopes and capabilities.
2. Authorization Server Discovery: The client retrieves the OAuth server metadata (token endpoint, authorization endpoint, supported PKCE methods) through LiteLLM’s .well-known/oauth-authorization-server endpoint.
3. Dynamic Client Registration: The client registers through LiteLLM, which forwards the request to the authorization server (RFC 7591). If the provider doesn’t support dynamic registration, you can pre-store client_id/client_secret in LiteLLM (e.g., GitHub MCP) and the flow proceeds the same way.
4. User Authorization: The client launches a browser session (with code challenge and resource hints). The user approves access, the authorization server sends the code through LiteLLM back to the client.
5. Token Exchange: The client calls LiteLLM with the authorization code, code verifier, and resource. LiteLLM exchanges them with the authorization server and returns the issued access/refresh tokens.
6. MCP Invocation: With a valid token, the client sends the MCP JSON-RPC request (plus LiteLLM API key) to LiteLLM, which forwards it to the MCP server and relays the tool response.

See the official MCP Authorization Flow for additional reference.

Forwarding Custom Headers to MCP Servers

LiteLLM supports forwarding additional custom headers from MCP clients to backend MCP servers using the extra_headers configuration parameter. This allows you to pass custom authentication tokens, API keys, or other headers that your MCP server requires.

Configuration

mcp_servers:
  github_mcp:
    url: "https://api.githubcopilot.com/mcp"
    auth_type: "bearer_token"
    auth_value: "ghp_default_token"
    extra_headers: ["custom_key", "x-custom-header", "Authorization"]
    description: "GitHub MCP server with custom header forwarding"

Use this when giving users access to a group of MCP servers.

Format: x-mcp-{server_alias}-{header_name}: value

This allows you to use different authentication for different MCP servers.

Examples:

• x-mcp-github-authorization: Bearer ghp_xxxxxxxxx - GitHub MCP server with Bearer token
• x-mcp-zapier-x-api-key: sk-xxxxxxxxx - Zapier MCP server with API key
• x-mcp-deepwiki-authorization: Basic base64_encoded_creds - DeepWiki MCP server with Basic auth

from fastmcp import Client
import asyncio

# Standard MCP configuration with multiple servers
config = {
    "mcpServers": {
        "mcp_group": {
            "url": "http://localhost:4000/mcp",
            "headers": {
                "x-mcp-servers": "dev_group", # assume this gives access to github, zapier and deepwiki
                "x-litellm-api-key": "Bearer sk-1234",
                "x-mcp-github-authorization": "Bearer gho_token", 
                "x-mcp-zapier-x-api-key": "sk-xxxxxxxxx",
                "x-mcp-deepwiki-authorization": "Basic base64_encoded_creds",
                "custom_key": "value"
            }
        }
    }
}

# Create a client that connects to all servers
client = Client(config)


async def main():
    async with client:
        tools = await client.list_tools()
        print(f"Available tools: {tools}")

        # call mcp 
        await client.call_tool(
            name="github_mcp-search_issues",
            arguments={'query': 'created:>2024-01-01', 'sort': 'created', 'order': 'desc', 'perPage': 30}
        )

if __name__ == "__main__":
    asyncio.run(main())

Benefits:

• Server-specific authentication: Each MCP server can use different auth methods
• Better security: No need to share the same auth token across all servers
• Flexible header names: Support for different auth header types (authorization, x-api-key, etc.)
• Clean separation: Each server's auth is clearly identified

Client Usage

When connecting from MCP clients, include the custom headers that match the extra_headers configuration:

from fastmcp import Client
import asyncio

# MCP client configuration with custom headers
config = {
    "mcpServers": {
        "github": {
            "url": "http://localhost:4000/github_mcp/mcp",
            "headers": {
                "x-litellm-api-key": "Bearer sk-1234",
                "Authorization": "Bearer gho_token", 
                "custom_key": "custom_value",
                "x-custom-header": "additional_data"
            }
        }
    }
}

# Create a client that connects to the server
client = Client(config)

async def main():
    async with client:
        # List available tools
        tools = await client.list_tools()
        print(f"Available tools: {tools}")
        
        # Call a tool if available
        if tools:
            result = await client.call_tool(tools[0].name, {})
            print(f"Tool result: {result}")

# Run the client
asyncio.run(main())

{
  "mcpServers": {
    "GitHub": {
      "url": "http://localhost:4000/github_mcp/mcp",
      "headers": {
        "x-litellm-api-key": "Bearer $LITELLM_API_KEY",
        "Authorization": "Bearer $GITHUB_TOKEN",
        "custom_key": "custom_value",
        "x-custom-header": "additional_data"
      }
    }
  }
}

curl --location 'http://localhost:4000/github_mcp/mcp' \
--header 'Content-Type: application/json' \
--header 'x-litellm-api-key: Bearer sk-1234' \
--header 'Authorization: Bearer gho_token' \
--header 'custom_key: custom_value' \
--header 'x-custom-header: additional_data' \
--data '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/list"
}'

How It Works

1. Configuration: Define extra_headers in your MCP server config with the header names you want to forward
2. Client Headers: Include the corresponding headers in your MCP client requests
3. Header Forwarding: LiteLLM automatically forwards matching headers to the backend MCP server
4. Authentication: The backend MCP server receives both the configured auth headers and the custom headers

Passing Request Headers to STDIO env Vars

If your stdio MCP server needs per-request credentials, you can map HTTP headers from the client request directly into the environment for the launched stdio process. Reference the header name in the env value using the ${X-HEADER_NAME} syntax. LiteLLM will read that header from the incoming request and set the env var before starting the command.

{
  "mcpServers": {
    "github": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "GITHUB_PERSONAL_ACCESS_TOKEN",
        "ghcr.io/github/github-mcp-server"
      ],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "${X-GITHUB_PERSONAL_ACCESS_TOKEN}"
      }
    }
  }
}

In this example, when a client makes a request with the X-GITHUB_PERSONAL_ACCESS_TOKEN header, the proxy forwards that value into the stdio process as the GITHUB_PERSONAL_ACCESS_TOKEN environment variable.

Using your MCP with client side credentials

Use this if you want to pass a client side authentication token to LiteLLM to then pass to your MCP to auth to your MCP.

New Server-Specific Auth Headers (Recommended)

You can specify MCP auth tokens using server-specific headers in the format x-mcp-{server_alias}-{header_name}. This allows you to use different authentication for different MCP servers.

Benefits:

• Server-specific authentication: Each MCP server can use different auth methods
• Better security: No need to share the same auth token across all servers
• Flexible header names: Support for different auth header types (authorization, x-api-key, etc.)
• Clean separation: Each server's auth is clearly identified

Legacy Auth Header (Deprecated)

You can also specify your MCP auth token using the header x-mcp-auth. This will be forwarded to all MCP servers and is deprecated in favor of server-specific headers.

Connect via OpenAI Responses API with Server-Specific Auth

Use the OpenAI Responses API and include server-specific auth headers:

curl --location 'https://api.openai.com/v1/responses' \
--header 'Content-Type: application/json' \
--header "Authorization: Bearer $OPENAI_API_KEY" \
--data '{
    "model": "gpt-4o",
    "tools": [
        {
            "type": "mcp",
            "server_label": "litellm",
            "server_url": "litellm_proxy",
            "require_approval": "never",
            "headers": {
                "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY",
                "x-mcp-github-authorization": "Bearer YOUR_GITHUB_TOKEN",
                "x-mcp-zapier-x-api-key": "YOUR_ZAPIER_API_KEY"
            }
        }
    ],
    "input": "Run available tools",
    "tool_choice": "required"
}'

Connect via OpenAI Responses API with Legacy Auth

Use the OpenAI Responses API and include the x-mcp-auth header for your MCP server authentication:

curl --location 'https://api.openai.com/v1/responses' \
--header 'Content-Type: application/json' \
--header "Authorization: Bearer $OPENAI_API_KEY" \
--data '{
    "model": "gpt-4o",
    "tools": [
        {
            "type": "mcp",
            "server_label": "litellm",
            "server_url": "litellm_proxy",
            "require_approval": "never",
            "headers": {
                "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY",
                "x-mcp-auth": YOUR_MCP_AUTH_TOKEN
            }
        }
    ],
    "input": "Run available tools",
    "tool_choice": "required"
}'

Connect via LiteLLM Proxy Responses API with Server-Specific Auth

Use this when calling LiteLLM Proxy for LLM API requests to /v1/responses endpoint with server-specific authentication:

curl --location '<your-litellm-proxy-base-url>/v1/responses' \
--header 'Content-Type: application/json' \
--header "Authorization: Bearer $LITELLM_API_KEY" \
--data '{
    "model": "gpt-4o",
    "tools": [
        {
            "type": "mcp",
            "server_label": "litellm",
            "server_url": "litellm_proxy",
            "require_approval": "never",
            "headers": {
                "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY",
                "x-mcp-github-authorization": "Bearer YOUR_GITHUB_TOKEN",
                "x-mcp-zapier-x-api-key": "YOUR_ZAPIER_API_KEY"
            }
        }
    ],
    "input": "Run available tools",
    "tool_choice": "required"
}'

Connect via LiteLLM Proxy Responses API with Legacy Auth

Use this when calling LiteLLM Proxy for LLM API requests to /v1/responses endpoint with MCP authentication:

curl --location '<your-litellm-proxy-base-url>/v1/responses' \
--header 'Content-Type: application/json' \
--header "Authorization: Bearer $LITELLM_API_KEY" \
--data '{
    "model": "gpt-4o",
    "tools": [
        {
            "type": "mcp",
            "server_label": "litellm",
            "server_url": "litellm_proxy",
            "require_approval": "never",
            "headers": {
                "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY",
                "x-mcp-auth": "YOUR_MCP_AUTH_TOKEN"
            }
        }
    ],
    "input": "Run available tools",
    "tool_choice": "required"
}'

Connect via Cursor IDE with Server-Specific Auth

Use tools directly from Cursor IDE with LiteLLM MCP and include server-specific authentication:

Setup Instructions:

1. Open Cursor Settings: Use ⇧+⌘+J (Mac) or Ctrl+Shift+J (Windows/Linux)
2. Navigate to MCP Tools: Go to the "MCP Tools" tab and click "New MCP Server"
3. Add Configuration: Copy and paste the JSON configuration below, then save with Cmd+S or Ctrl+S

{
  "mcpServers": {
    "LiteLLM": {
      "url": "litellm_proxy",
      "headers": {
        "x-litellm-api-key": "Bearer $LITELLM_API_KEY",
        "x-mcp-github-authorization": "Bearer $GITHUB_TOKEN",
        "x-mcp-zapier-x-api-key": "$ZAPIER_API_KEY"
      }
    }
  }
}

Connect via Cursor IDE with Legacy Auth

Use tools directly from Cursor IDE with LiteLLM MCP and include your MCP authentication token:

Setup Instructions:

1. Open Cursor Settings: Use ⇧+⌘+J (Mac) or Ctrl+Shift+J (Windows/Linux)
2. Navigate to MCP Tools: Go to the "MCP Tools" tab and click "New MCP Server"
3. Add Configuration: Copy and paste the JSON configuration below, then save with Cmd+S or Ctrl+S

{
  "mcpServers": {
    "LiteLLM": {
      "url": "litellm_proxy",
      "headers": {
        "x-litellm-api-key": "Bearer $LITELLM_API_KEY",
        "x-mcp-auth": "$MCP_AUTH_TOKEN"
      }
    }
  }
}

Connect via Streamable HTTP Transport with Server-Specific Auth

Connect to LiteLLM MCP using HTTP transport with server-specific authentication:

Server URL:

litellm_proxy

Headers:

x-litellm-api-key: Bearer YOUR_LITELLM_API_KEY
x-mcp-github-authorization: Bearer YOUR_GITHUB_TOKEN
x-mcp-zapier-x-api-key: YOUR_ZAPIER_API_KEY

Connect via Streamable HTTP Transport with Legacy Auth

Connect to LiteLLM MCP using HTTP transport with MCP authentication:

Server URL:

litellm_proxy

Headers:

x-litellm-api-key: Bearer YOUR_LITELLM_API_KEY
x-mcp-auth: Bearer YOUR_MCP_AUTH_TOKEN

This URL can be used with any MCP client that supports HTTP transport. The x-mcp-auth header will be forwarded to your MCP server for authentication.

Connect via Python FastMCP Client with Server-Specific Auth

Use the Python FastMCP client to connect to your LiteLLM MCP server with server-specific authentication:

import asyncio
import json

from fastmcp import Client
from fastmcp.client.transports import StreamableHttpTransport

# Create the transport with your LiteLLM MCP server URL and server-specific auth headers
server_url = "litellm_proxy"
transport = StreamableHttpTransport(
    server_url,
    headers={
        "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY",
        "x-mcp-github-authorization": "Bearer YOUR_GITHUB_TOKEN",
        "x-mcp-zapier-x-api-key": "YOUR_ZAPIER_API_KEY"
    }
)

# Initialize the client with the transport
client = Client(transport=transport)


async def main():
    # Connection is established here
    print("Connecting to LiteLLM MCP server with server-specific authentication...")
    async with client:
        print(f"Client connected: {client.is_connected()}")

        # Make MCP calls within the context
        print("Fetching available tools...")
        tools = await client.list_tools()

        print(f"Available tools: {json.dumps([t.name for t in tools], indent=2)}")
        
        # Example: Call a tool (replace 'tool_name' with an actual tool name)
        if tools:
            tool_name = tools[0].name
            print(f"Calling tool: {tool_name}")
            
            # Call the tool with appropriate arguments
            result = await client.call_tool(tool_name, arguments={})
            print(f"Tool result: {result}")


# Run the example
if __name__ == "__main__":
    asyncio.run(main())

Connect via Python FastMCP Client with Legacy Auth

Use the Python FastMCP client to connect to your LiteLLM MCP server with MCP authentication:

import asyncio
import json

from fastmcp import Client
from fastmcp.client.transports import StreamableHttpTransport

# Create the transport with your LiteLLM MCP server URL and auth headers
server_url = "litellm_proxy"
transport = StreamableHttpTransport(
    server_url,
    headers={
        "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY",
        "x-mcp-auth": "Bearer YOUR_MCP_AUTH_TOKEN"
    }
)

# Initialize the client with the transport
client = Client(transport=transport)


async def main():
    # Connection is established here
    print("Connecting to LiteLLM MCP server with authentication...")
    async with client:
        print(f"Client connected: {client.is_connected()}")

        # Make MCP calls within the context
        print("Fetching available tools...")
        tools = await client.list_tools()

        print(f"Available tools: {json.dumps([t.name for t in tools], indent=2)}")
        
        # Example: Call a tool (replace 'tool_name' with an actual tool name)
        if tools:
            tool_name = tools[0].name
            print(f"Calling tool: {tool_name}")
            
            # Call the tool with appropriate arguments
            result = await client.call_tool(tool_name, arguments={})
            print(f"Tool result

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