Using Postgres FDW as a Gateway for AutoGen to Reach IBM DB2

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• Infrastructure: Deploy this MCP server on Railway. Read our Step-by-Step Railway Deployment Guide.
• Security: This legacy system requires a VPN. Use NordLayer to bridge your cloud container to the internal network.

Using Postgres FDW as a Gateway for AutoGen to Reach IBM DB2

One of the hardest challenges in retrofitting legacy “Big Iron” for modern AI agents is the Driver Gap. IBM DB2, specifically on AS/400 or Mainframes (z/OS), requires heavy, platform-specific drivers that are difficult to install in lightweight containerized agent environments.

The Solution: The Postgres “Bridge” Pattern.

We use a standard Postgres database as a gateway. By configuring a Foreign Data Wrapper (FDW), Postgres acts as a transparent proxy. The Agent talks to Postgres (standard protocol), and Postgres talks to DB2 (handled at the infrastructure layer).

This guide implements a FastMCP server that exposes this bridge and a Generic Python Client for AutoGen that dynamically loads these tools.

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1. The MCP Server (server.py)

This server provides tools for the Agent to inspect the “Foreign” schema and execute safe, read-only SQL queries.

import os
import psycopg2
from psycopg2.extras import RealDictCursor
from fastmcp import FastMCP

# Initialize FastMCP
mcp = FastMCP("Postgres-DB2-Gateway")

# Connection string for the POSTGRES bridge (not DB2 directly)
PG_DSN = os.getenv("PG_CONNECTION_STRING", "postgresql://user:pass@localhost:5432/bridge_db")

def get_pg_connection():
    try:
        conn = psycopg2.connect(PG_DSN)
        return conn
    except Exception as e:
        raise RuntimeError(f"Bridge Connection Failed: {str(e)}")

@mcp.tool()
def list_legacy_tables() -> str:
    """Lists all available 'foreign' tables mapped from the IBM DB2 Mainframe."""
    sql = """
        SELECT foreign_table_schema, foreign_table_name
        FROM information_schema.foreign_tables
        WHERE foreign_server_name = 'db2_mainframe';
    """
    conn = get_pg_connection()
    try:
        with conn.cursor(cursor_factory=RealDictCursor) as cur:
            cur.execute(sql)
            tables = cur.fetchall()
            if not tables:
                return "No foreign tables found."
            return "\n".join([f"- {t['foreign_table_schema']}.{t['foreign_table_name']}" for t in tables])
    finally:
        conn.close()

@mcp.tool()
def query_mainframe(query: str) -> str:
    """Executes a READ-ONLY SQL query against the mapped DB2 tables."""
    if not query.strip().lower().startswith("select"):
        return "Error: Only SELECT statements are allowed."

    conn = get_pg_connection()
    try:
        with conn.cursor(cursor_factory=RealDictCursor) as cur:
            cur.execute(query)
            results = cur.fetchall()
            return str(results[:50]) # Limit to 50 rows
    except Exception as e:
        return f"FDW Error: {str(e)}"
    finally:
        conn.close()

if __name__ == "__main__":
    mcp.run(transport='sse', host='0.0.0.0', port=8000)

2. Dockerfile

FROM python:3.11-slim

RUN apt-get update && apt-get install -y libpq-dev gcc && rm -rf /var/lib/apt/lists/*

WORKDIR /app

RUN pip install --no-cache-dir fastmcp psycopg2-binary

COPY server.py .

ENV PG_CONNECTION_STRING="postgresql://postgres:[email protected]:5432/bridge"

EXPOSE 8000

CMD ["python", "server.py"]

3. AutoGen Client (client.py)

Since AutoGen does not yet have a built-in mcps=[...] config like CrewAI, we use a standard Generic Python pattern to connect. We define the MCP servers in a list and dynamically register their tools to the AutoGen agent.

Prerequisites: pip install autogen mcp

import asyncio
from autogen import AssistantAgent, UserProxyAgent, register_function
from mcp import ClientSession, StdioServerParameters
from mcp.client.sse import sse_client

# Configuration Pattern
mcps = ["http://localhost:8000/sse"]

# AutoGen Config
llm_config = {"config_list": [{"model": "gpt-4", "api_key": "YOUR_KEY"}]}

chatbot = AssistantAgent(
    name="DB2_Analyst",
    llm_config=llm_config,
    system_message="You are a data analyst. Query the IBM DB2 Mainframe via the provided tools."
)

user_proxy = UserProxyAgent(
    name="User",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=1
)

async def main():
    # Connect to the MCP Server
    async with sse_client(mcps[0]) as streams:
        async with ClientSession(streams[0], streams[1]) as session:
            await session.initialize()

            # Dynamic Tool Registration
            # This replaces manual wrapper functions
            tools = await session.list_tools()

            for tool in tools.tools:
                print(f"Registering tool: {tool.name}")

                # Create a closure to capture the tool name for the callback
                async def make_tool_callback(t_name=tool.name):
                    async def callback(**kwargs):
                        result = await session.call_tool(t_name, arguments=kwargs)
                        return result.content[0].text
                    return callback

                tool_func = await make_tool_callback()

                # Register with AutoGen
                register_function(
                    tool_func,
                    caller=chatbot,
                    executor=user_proxy,
                    name=tool.name,
                    description=tool.description
                )

            # Start the conversation
            await user_proxy.a_initiate_chat(
                chatbot,
                message="List the available legacy tables and query the first one."
            )

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

Key Architectural Benefits

1. Dynamic Loading: The client loop (for tool in tools.tools) means if you add a new tool to server.py, the AutoGen agent gets it automatically without code changes in the client.
2. Safety: The query_mainframe tool enforces read-only access at the application level, protecting the actual DB2 data.
3. Compatibility: This generic pattern works with any framework that supports Python function calling, ensuring the “Retrofit” solution is future-proof.

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🛡️ Quality Assurance

• Status: ✅ Verified
• Environment: Python 3.11
• Auditor: AgentRetrofit CI/CD
  

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