AutoGen for processing COBOL Flat Files in multi-agent workflows

Deployment Tips

• Infrastructure: Deploy this MCP server on Railway

AutoGen for Processing COBOL Flat Files in Multi-Agent Workflows

The “Big Iron” Barrier

Modern AI agents, like those built with Microsoft’s AutoGen, are excellent at reasoning and JSON manipulation. However, they hit a brick wall when facing the backbone of global finance and logistics: COBOL Flat Files.

These files don’t use delimiters (CSVs) or key-value pairs (JSON). They rely on fixed-width positional layouts and often use legacy encodings like EBCDIC. An AutoGen workflow will hallucinate wildly if you simply feed it a raw stream of fixed-width text.

The Solution: The “Cobol-Parser” MCP

Instead of rewriting the COBOL logic, we create a FastMCP server that acts as a translation layer. This server provides tools to:

1. Decode EBCDIC byte streams into ASCII/UTF-8.
2. Parse Fixed-Width Records using a definable schema (Copybook-style).
3. Return Structured JSON that AutoGen agents can natively understand and process.

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1. The Integration Architecture

In this architecture, the MCP server does the heavy lifting of data normalization.

1. Ingestion Agent: Retrieves the raw file from an FTP/Mainframe volume.
2. Processing Agent: Calls the MCP tool parse_flat_file_record.
3. MCP Server: Applies the byte-level parsing logic using Python’s standard library.
4. Result: Clean JSON is returned to the agent for downstream tasks (e.g., database insertion, analytics).

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2. The Bridge Code (server.py)

We use FastMCP to expose the parsing logic. We rely on Python’s built-in string and byte methods to handle the conversion, avoiding unnecessary external dependencies.

from typing import List, Dict, Any
from fastmcp import FastMCP
from pydantic import BaseModel, Field

# 1. Initialization
mcp = FastMCP("CobolBridge")

# --- Schemas ---

class FieldDefinition(BaseModel):
    name: str = Field(description="The name of the field (e.g., 'CUSTOMER_ID')")
    start: int = Field(description="Start position (0-indexed, inclusive)")
    length: int = Field(description="Length of the field")
    type: str = Field(description="Data type: 'string', 'integer', or 'decimal'")

# --- Tools ---

@mcp.tool()
def convert_ebcdic_to_ascii(hex_data: str, encoding: str = "cp037") -> str:
    """
    Converts a HEX string representation of EBCDIC data into an ASCII string.
    Common encodings: 'cp037' (US EBCDIC), 'cp500' (International).
    """
    try:
        # Convert hex string to bytes
        byte_data = bytes.fromhex(hex_data)
        # Decode using the specified EBCDIC code page
        # Python stdlib supports cp037, cp500, etc. natively
        return byte_data.decode(encoding)
    except Exception as e:
        return f"Error decoding data: {str(e)}"

@mcp.tool()
def parse_fixed_width_record(record: str, layout: List[FieldDefinition]) -> Dict[str, Any]:
    """
    Parses a single text line based on a provided Copybook-style layout.
    Returns a JSON dictionary representing the record.
    """
    parsed_record = {}

    for field in layout:
        # Slicing the string based on start and length
        end = field.start + field.length

        # Guard against short records
        if len(record) < field.start:
            raw_value = ""
        else:
            raw_value = record[field.start:end].strip()

        # Type conversion
        try:
            if field.type == 'integer':
                parsed_record[field.name] = int(raw_value) if raw_value else 0
            elif field.type == 'decimal':
                parsed_record[field.name] = float(raw_value) if raw_value else 0.0
            else:
                parsed_record[field.name] = raw_value
        except ValueError:
            # Fallback for bad data
            parsed_record[field.name] = raw_value

    return parsed_record

@mcp.tool()
def get_sample_copybook() -> List[FieldDefinition]:
    """
    Returns a sample layout for a Customer Master record to help the agent understand the structure.
    """
    return [
        FieldDefinition(name="RECORD_TYPE", start=0, length=2, type="string"),
        FieldDefinition(name="CUSTOMER_ID", start=2, length=10, type="integer"),
        FieldDefinition(name="CUSTOMER_NAME", start=12, length=30, type="string"),
        FieldDefinition(name="LAST_ORDER_AMT", start=42, length=12, type="decimal"),
    ]

# 3. Execution
if __name__ == "__main__":
    mcp.run()

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3. The Container (Dockerfile)

To deploy this in a modern stack (like Railway, AWS ECS, or Kubernetes), we containerize it.

Crucial Note: We explicitly EXPOSE 8000 to ensure the AutoGen environment can reach the MCP server via SSE or HTTP. We install pydantic explicitly as it is a dependency for our schemas.

# Use a lightweight Python base
FROM python:3.11-slim

# Set working directory
WORKDIR /app

# Install dependencies
# 'fastmcp' is the core framework.
# 'pydantic' is used for data validation in schemas.
RUN pip install --no-cache-dir fastmcp pydantic

# Copy the server code
COPY server.py .

# Expose the default FastMCP port
EXPOSE 8000

# Run the server
CMD ["python", "server.py"]

4. How to Use in AutoGen

Once the Docker container is running, you connect your UserProxyAgent to the MCP server configuration.

Example Conversation Flow:

1. User: “I have a file dump.dat containing EBCDIC hex strings. Parse the first record.”
2. Agent: Calls convert_ebcdic_to_ascii with the hex string.
3. MCP: Returns the decoded fixed-width string (e.g., 0100004928ACME CORP 0000150.00).
4. Agent: “Now I need to parse this string using the Customer Master layout.”
5. Agent: Calls get_sample_copybook to get the schema.
6. Agent: Calls parse_fixed_width_record with the string and the schema.
7. MCP: Returns:

   {
     "RECORD_TYPE": "01",
     "CUSTOMER_ID": 4928,
     "CUSTOMER_NAME": "ACME CORP",
     "LAST_ORDER_AMT": 150.00
   }

8. Agent: Uses this JSON to update the modern CRM.

Troubleshooting Legacy Data

• “Garbage” Characters: If convert_ebcdic_to_ascii returns nonsense, the source might be using a different Code Page (e.g., CP500 for Canada/Europe vs CP037 for US). Ask the agent to retry with cp500.
• Packed Decimal (COMP-3): This simple parser handles text integers. If your COBOL file contains binary packed decimals (COMP-3), you must unpack the bytes before decoding to ASCII. We recommend adding a specific unpack_comp3 tool if dealing with raw binary dumps.

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🛡️ Quality Assurance

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

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