MCP Server

Unified has launched an MCP server that connects any Unified connection to LLM (Large Language Model) providers supporting the newest MCP protocols. The available MCP tools will be determined by the integration’s feature support and the connection’s requested permissions.

The Unified MCP server sits on top of our Unified API (which calls individual APIs), and hides all of the complexity of API calling. Each call to a tool will count as 1 API request on your plan.

Warning: Our MCP server is in beta and shouldn't be used for production systems (yet). Reach out to us if you would like to use it in your product application.

URLs

• Streamable HTTP: https://mcp-api.unified.to/mcp
• SSE: https://mcp-api.unified.to/sse
• stdin: for real? it's 2025...

Authentication

You must provide a token to the MCP server either as a token URL parameter (eg. ?token={token}) or in the Authorization header as a bearer token (eg. Authorization: bearer {token})

The token is generated as follows:{connection_ID}-{nonce}-{signature}

Installation & Usage

OpenAI API:

OpenAI's chat-completion API supports remote MCP directly. Enter in our Streamable HTTP URL.

resp = client.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "mcp",
        "server_label": "unifiedMCP",
        "server_url": "https://mcp-api.unified.to/mcp?token=XXXXXXXX",
        "require_approval": "never",
        "allowed_tools": [],
    }],
    input="list the candidates and then analyse the resumes from their applications",
)

OpenAI also supports sending in a list of MCP tools and having their API request that you call a specific tool with specific parameters. You would first call our MCP /tools endpoint, then take the output and include it in your prompt API call:

resp = client.responses.create(
    model="gpt-4.1",
    tools=$TOOLS,
    input="list the candidates and then analyse the resumes from their applications",
)

Once you call that MCP tool (using our /tools/{id}/call endpoint), you would create a new prompt and reference the original responce with a previous_response_id value.

Please see this article for more information.

Anthropic API:

Anthropic's chat-completion API allows for the addition of MCP tools and will return back an intermediate response asking you to call that MCP tool and then continue the request by providing its output.

You would first call our MCP /tools endpoint, then take the output and include it in your prompt API call:

resp = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    tools=$TOOLS,
    input="list the candidates and then analyse the resumes from their applications",
)

Anthropic's API will then return a response with a tool_use content blocks:

[
  {
    "type": "tool_use",
    "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV",
    "name": "list_candidates",
    "input": { "limit": "100" }
  }
]

Once you call that MCP tool (using our /tools/{id}/call endpoint), you would return the following back to the model in a subsequent user message:

[
  {
    "type": "tool_result",
    "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV",
    "content": "..."
  }
]

Please see this article for more information. When creating a chat completion message, include the tools field with a list of tools.

Gemini will return a request for you to call that specific tool:

content {
    role: "model"
    parts {
      function_call {
        name: "list_candidates"
        args {
          fields {
            key: "limit"
            value {
              string_value: "100"
            }
          }
        }
      }
    }
}

When you respond back with another message creation, you can include the MCP's response in the content array:

{
  "role": "user",
  "parts": [
    {
      "functionResponse": {
        "name": "list_candidates",
        "response": {
          ...
        }
      }
    }
  ]
}

Google Gemini API:

Google Gemini uses a similar concept that OpenAI and Anthropic use for tools, but they call it function_declarations.

Please see this article for more information.

Claude.ai (online):

Go to claude.ai, then navigate to Settings > Integrations. Click on "Add custom integration". Enter the MCP URL:

https://mcp-api.unified.to/sse?token={connectionID}-{nonce}-{signature}

Make sure to provide your end-customer the appropriate token value.

Claude (desktop client):

Edit the claude_desktop_config.json file:

{
    "mcpServers": {
        "unified-mcp": {
            "command": "npx",
            "args": [
                "-y",
                "mcp-remote",
                "https://mcp-api.unified.to/sse?token={connectionID}-{nonce}-{signature}",
                "--allow-http"
            ]
        }
    }
}

Make sure to provide your end-customer the appropriate token value.

Cursor:

Navigate to Cursor > Settings > Cursor Settings > MCP and edit the MCP configuration. Replace unified-mcp with the name of your own application and then make sure to provide your end-customer the appropriate token value.

{
    "mcpServers": {
        "unified-mcp": {
            "url": "https://mcp-api.unified.to/sse?token={connectionID}-{nonce}-{signature}"
        }
    }
}

MCP is a new protocol and it is moving fast. We expect more LLM & agent clients to support its newer Streamable HTTP transport protocol. We also expect that the MCP protocol will continue to expand quickly. Stay tuned as we also keep up.

Additional API Endpoints

GET /tools

Get a list of the MCP tools associated with the connection. The payload will include an object with the parameters with name as the key and value. The result is an array of:

{
    id: string;
    description: string;
    parameters: {
        name: string;
        description: string;
        required: boolean;
    }[]

}

POST /tools/{id}/call

Call that tools and return the result.

{
    content: {
        type: 'text';
        text: string;
    }[],
    data: JSON-object
}[]