template: Added MCP orchestrate AutoAI template

agents/community/mcp-orchestrate-autoai-template/agent.yaml

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+spec_version: v1
+kind: native
+name: iris_prediction_agent
+description: |
+  Predicts iris petal width from sepal length, sepal width, petal length, and species
+  using a watsonx.ai deployed machine learning model.
+llm: groq/openai/gpt-oss-120b
+style: react
+hide_reasoning: false
+instructions: |
+  You are an iris petal width prediction assistant.
+
+  STRICT RULES — follow these without exception:
+  1. NEVER answer a petal width question from your own knowledge or reasoning.
+  2. NEVER ask the user to confirm inputs — if all 4 values are present, call the tool immediately.
+  3. ALWAYS call get_petal_width_from_iris_data when the user provides:
+     - sepal_length (float, cm)
+     - sepal_width (float, cm)
+     - petal_length (float, cm)
+     - species (one of: setosa, versicolor, virginica)
+  4. If and only if one or more of the 4 values above is missing, ask ONLY for the missing ones.
+  5. After the tool returns a result, report it as: "The predicted petal width is X cm."
+  6. Do NOT call any other tool or perform any calculation yourself.
+tools:
+  - iris-toolkit:get_petal_width_from_iris_data
+collaborators: []

agents/community/mcp-orchestrate-autoai-template/mcp_server/requirements.txt

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+mcp
+python-dotenv
+ibm-watsonx-ai
+pydantic

agents/community/mcp-orchestrate-autoai-template/mcp_server/server.py

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+from mcp.server.fastmcp import FastMCP
+from utils import (
+    IrisInformation,
+    prepare_api_client,
+    get_petal_width_deployment_id,
+    build_scoring_payload,
+    extract_petal_width,
+)
+
+mcp = FastMCP("iris-toolkit")
+api_client = prepare_api_client()
+
+
+@mcp.tool()
+def get_petal_width_from_iris_data(iris_information: IrisInformation) -> float:
+    """
+    Predict the petal width (in cm) of an iris flower using a watsonx.ai deployed model.
+
+    Provide sepal_length, sepal_width, petal_length (all in cm as floats),
+    and species (one of: setosa, versicolor, virginica).
+
+    Returns the predicted petal width as a float.
+    """
+    deployment_id = get_petal_width_deployment_id()
+    payload = build_scoring_payload(iris_information)
+    response = api_client.deployments.score(deployment_id, meta_props=payload)
+    return extract_petal_width(response)
+
+
+if __name__ == "__main__":
+    mcp.run(transport="stdio")

agents/community/mcp-orchestrate-autoai-template/mcp_server/utils.py

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+import os
+from dotenv import load_dotenv
+from typing import Literal
+from pydantic import BaseModel, Field
+
+
+class IrisInformation(BaseModel):
+    sepal_length: float = Field(..., description="Sepal length in cm")
+    sepal_width: float = Field(..., description="Sepal width in cm")
+    petal_length: float = Field(..., description="Petal length in cm")
+    species: Literal["setosa", "versicolor", "virginica"] = Field(
+        ..., description="Iris species: setosa, versicolor, or virginica"
+    )
+
+
+def prepare_api_client():
+    from ibm_watsonx_ai import APIClient, Credentials
+
+    load_dotenv()
+
+    api_client = APIClient(
+        credentials=Credentials(
+            url=os.getenv("WATSONX_URL"),
+            api_key=os.getenv("WATSONX_API_KEY"),
+        ),
+        space_id=os.getenv("WATSONX_SPACE_ID"),
+    )
+    return api_client
+
+
+def get_petal_width_deployment_id() -> str:
+    load_dotenv()
+    deployment_id = os.getenv("WATSONX_PETAL_WIDTH_DEPLOYMENT_ID")
+    if not deployment_id:
+        raise ValueError("WATSONX_PETAL_WIDTH_DEPLOYMENT_ID is not set")
+    return deployment_id
+
+
+def build_scoring_payload(iris: IrisInformation) -> dict:
+    """Build the scoring payload in the format expected by watsonx.ai deployment."""
+    return {
+        "input_data": [
+            {
+                "fields": ["sepal_length", "sepal_width", "petal_length", "species"],
+                "values": [
+                    [
+                        iris.sepal_length,
+                        iris.sepal_width,
+                        iris.petal_length,
+                        iris.species,
+                    ]
+                ],
+            }
+        ]
+    }
+
+
+def extract_petal_width(response: dict) -> float:
+    """Extract the predicted petal width float from the watsonx.ai scoring response."""
+    try:
+        predictions = response["predictions"][0]
+        values = predictions["values"][0]
+        # The deployment returns a list of predicted values; petal width is the first
+        return float(values[0])
+    except (KeyError, IndexError, TypeError, ValueError) as e:
+        raise RuntimeError(
+            f"Unexpected response structure from deployment: {response}"
+        ) from e

agents/community/mcp-orchestrate-autoai-template/toolkit.yaml

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+spec_version: v1
+kind: mcp
+name: iris-toolkit
+description: "Iris petal width prediction using watsonx.ai deployment"
+command: python server.py
+env:
+  - WATSONX_URL
+  - WATSONX_API_KEY
+  - WATSONX_SPACE_ID
+  - WATSONX_PETAL_WIDTH_DEPLOYMENT_ID
+tools:
+  - "*"
+package_root: ./mcp_server