Ecosystem Runtime MCP

The Ecosystem Runtime MCP provides an MCP interface to the ecosystem Runtime as well as providing a python interface which can be used to provide custom APIs to the ecosystem Runtime.

Installing the Runtime MCP

The Runtime MCP can be added to your ecosystem environment using a configuration similar to the docker compose snippet below

  ecosystem-runtime-solo:
    image: ecosystemai/ecosystem-runtime-mcp:arm64
    container_name: ecosystem-runtime
    restart: unless-stopped
    environment:
      RUNTIME_URL: 'http://ecosystem-runtime-backend:8081'
      PORT: 8091
      MLFLOW_TRACKING_URI: 'http://mlflowurl:8085'
      RUNTIME_CONFIG: '/data/config/runtime_config.json'
      MONGO_CONNECT: ${MONGO_CONNECT_STRING}
    volumes:
      - ${DATA_PATH}:/data
    networks:
      - ecosystem
    ports:
      - "8091:8091"
    depends_on:
      ecosystem-runtime-backend:
        condition: service_healthy

For this configuration the OpenAPI style docs can be accessed at http://ecosystem-runtime:8091/docs and the MCP interface can be access at http://ecosystem-runtime:8091/mcp.

Environment Variables

The following environment variables can be used to set the behaviour of the Runtime MCP:

RUNTIME_URL: The master key for the ecosystem.Ai instance.
PORT: The port exposed by the Runtime MCP
MLFLOW_TRACKING_URI: The MLFlow link to use for downloading models
RUNTIME_CONFIG: The location of the config file giving the details of the MLFlow models to use
MONGO_CONNECT: The mongo connect string used by the runtime

Custom APIs

Custom APIs can be set up by creating a python file aligned with the following template

from fastapi import APIRouter, Body
from .custom_api_super import invocations, response
from ..type_models import Invocation, Response
import logging
 
# Create a logger instance
logger = logging.getLogger(__name__)
 
router = APIRouter()
 
 
@router.post(
    "/myCustomInvocationsName",
    operation_id="custom_invocations_call",
    description="Get a list of responses from the runtime",
    tags=["Predictors"]
    )
async def my_custom_invocations(
    customer: str = Body(..., description="The customer ID for the custom invocation."),
    params: str = Body(..., description="Parameters for the custom invocation, typically a JSON string.")
):
    """
    Custom endpoint to call /invocations on the runtime
    """
    body = Invocation(
        customer=customer,
        params=params
    )
    logger.info(f"Received request for custom invocations: {body}")
    return await invocations(body)
 
 
@router.post(
    "/myCustomResponseName",
    operation_id="custom_response_call",
    description="Send a response to the ecosystem.Ai runtime if the customer reaction generated using the result from the /invocations endpoint is a success",
    tags=["Predictors"]
    )
async def my_custom_response(
    body: Response = Body(..., description="The response to send to the runtime")
):
    """
    Customer endpoint to call /response on the runtime
    """
    logger.info(f"Received request for custom response: {body}")
    return await response(body)

This python file can be passed to the MCP using the workbench. When configuring plugins in a deployment, place your python file in the editor in the API tab and push the Compile button.

External Runtime Calls MLFlow Integration