Building a Data Engineering Agent with Claude Opus 4.7 and MCP

What Is a Data Engineering Agent?

A data engineering agent is an autonomous system that uses large language models (LLMs) to orchestrate data workflows—ingestion, transformation, validation, and loading—without manual intervention. Unlike traditional ETL pipelines, which follow rigid, pre-coded paths, agents reason about data problems, call tools dynamically, and adapt their approach based on results.

Think of it like this: a traditional pipeline is a assembly line with fixed stations. A data engineering agent is an intelligent worker who understands the goal, knows which tools are available, and decides which ones to use and in what order. When something unexpected happens—a schema change, a data quality issue, a new source—the agent can reason through it rather than fail.

Claude Opus 4.7, Anthropic’s latest flagship model, paired with the Model Context Protocol (MCP), makes building these agents practical. MCP is a standardized interface for connecting AI models to tools and data sources. Together, they let you build agents that ingest, transform, and validate data at scale without the platform overhead of enterprise BI tools like Looker or Tableau.

This matters because data teams at scale-ups and mid-market companies are drowning in tool sprawl. You might have Airflow for orchestration, dbt for transformation, Great Expectations for validation, and a separate BI layer. A well-designed agent can collapse multiple steps and reduce operational friction.

Why Claude Opus 4.7 and MCP?

Claude Opus 4.7 is purpose-built for agentic behavior. It excels at tool use—the ability to call functions, parse responses, and chain calls together. The model has strong reasoning capabilities, which matters when a data agent needs to diagnose why a transformation failed or decide which validation rule to apply first.

MCP standardizes how agents connect to external systems. Rather than building custom integrations for every data source, database, or API, you define tools once using the MCP spec, and any MCP-compatible client (including Claude) can use them. This is critical for data engineering because your agent needs to talk to databases, data warehouses, APIs, and file systems.

Together, Claude Opus 4.7 and MCP give you:

• Native tool calling: Claude can invoke functions with structured arguments and understand responses natively, without prompt hacking.
• Agentic reasoning: The model can plan multi-step workflows, recover from errors, and explain its decisions.
• Standardized integrations: MCP tools work across any compatible client, so your agent code is portable.
• Cost efficiency: Compared to running Looker or Tableau, a managed Claude API setup is significantly cheaper for mid-market teams.

For teams evaluating alternatives to Preset or other managed Superset providers, this agent pattern offers a way to embed AI-powered analytics and text-to-SQL capabilities without vendor lock-in.

Understanding the MCP Protocol

The Model Context Protocol is an open standard developed by Anthropic for connecting AI models to external tools and data. It’s simpler than it sounds.

An MCP server exposes resources and tools. Resources are data or state that the model can read (like a database schema or API documentation). Tools are functions the model can call (like “run a SQL query” or “validate a CSV”).

The protocol is transport-agnostic—it works over stdio, HTTP, or WebSocket. In practice, you write a Python or JavaScript server that implements the MCP spec, define your tools, and then any client (Claude, or another LLM) can discover and use them.

For a data engineering agent, your MCP server might expose tools like:

• Query execution: Run SQL against a Postgres or Snowflake database.
• File operations: Read/write Parquet, CSV, or JSON files from S3 or local storage.
• Data validation: Check row counts, schema compliance, null rates, and outliers.
• Schema inspection: Fetch table definitions, column types, and sample data.
• API calls: Invoke external data sources or trigger downstream processes.

The agent calls these tools, Claude interprets the results, and the agent decides what to do next. If a query fails, Claude can reason about why and retry differently. If validation catches an anomaly, Claude can log it, alert a human, or attempt a correction.

Setting Up Your Development Environment

Before you build, you need the right tools.

Install Python 3.10 or later and create a virtual environment:

python3 -m venv agent_env
source agent_env/bin/activate

Install the required packages:

pip install anthropic mcp psycopg2-binary pandas pyarrow

You’ll also need:

• An Anthropic API key (get one at https://console.anthropic.com/).
• A PostgreSQL database (or another SQL database; the patterns are the same).
• Optionally, the Claude Code CLI from the Claude Code GitHub Repository for testing agentic code generation.

Refer to the Anthropic Claude Documentation for the latest API details. The documentation covers tool calling, which is core to how agents work.

Building Your First MCP Server

Let’s build a minimal MCP server that exposes SQL query execution and schema inspection. This is the foundation of your data engineering agent.

Create a file called mcp_server.py:

import json
import psycopg2
from psycopg2.extras import RealDictCursor
from typing import Any, Dict, List

class DataEngineeringMCPServer:
    def __init__(self, db_config: Dict[str, str]):
        self.db_config = db_config
        self.connection = None
    
    def connect(self):
        """Establish database connection."""
        self.connection = psycopg2.connect(**self.db_config)
    
    def execute_query(self, query: str) -> Dict[str, Any]:
        """Execute a SQL query and return results."""
        try:
            with self.connection.cursor(cursor_factory=RealDictCursor) as cursor:
                cursor.execute(query)
                if query.strip().upper().startswith('SELECT'):
                    results = cursor.fetchall()
                    return {
                        'status': 'success',
                        'rows': [dict(row) for row in results],
                        'row_count': len(results)
                    }
                else:
                    self.connection.commit()
                    return {
                        'status': 'success',
                        'rows_affected': cursor.rowcount
                    }
        except Exception as e:
            return {'status': 'error', 'message': str(e)}
    
    def get_schema(self, table_name: str) -> Dict[str, Any]:
        """Fetch schema for a table."""
        try:
            query = f"""
            SELECT column_name, data_type, is_nullable
            FROM information_schema.columns
            WHERE table_name = %s
            ORDER BY ordinal_position
            """
            with self.connection.cursor(cursor_factory=RealDictCursor) as cursor:
                cursor.execute(query, (table_name,))
                columns = [dict(row) for row in cursor.fetchall()]
                return {'status': 'success', 'columns': columns}
        except Exception as e:
            return {'status': 'error', 'message': str(e)}
    
    def validate_data(self, table_name: str) -> Dict[str, Any]:
        """Run basic data quality checks."""
        try:
            validation_results = {}
            schema = self.get_schema(table_name)
            
            if schema['status'] != 'success':
                return schema
            
            columns = schema['columns']
            
            # Check row count
            count_result = self.execute_query(f"SELECT COUNT(*) as cnt FROM {table_name}")
            validation_results['total_rows'] = count_result['rows'][0]['cnt']
            
            # Check nulls per column
            for col in columns:
                col_name = col['column_name']
                null_result = self.execute_query(
                    f"SELECT COUNT(*) as null_count FROM {table_name} WHERE {col_name} IS NULL"
                )
                validation_results[f'{col_name}_nulls'] = null_result['rows'][0]['null_count']
            
            return {'status': 'success', 'validation': validation_results}
        except Exception as e:
            return {'status': 'error', 'message': str(e)}

# Initialize server
db_config = {
    'host': 'localhost',
    'database': 'analytics',
    'user': 'postgres',
    'password': 'your_password'
}

server = DataEngineeringMCPServer(db_config)
server.connect()

This server provides three tools: query execution, schema inspection, and data validation. Claude will call these tools to orchestrate your data pipeline.

Connecting Claude to Your MCP Server

Now create a client that connects Claude to your MCP server. This is where the agent logic lives.

Create a file called agent.py:

import json
from anthropic import Anthropic
from mcp_server import DataEngineeringMCPServer

client = Anthropic()

# Initialize your MCP server
db_config = {
    'host': 'localhost',
    'database': 'analytics',
    'user': 'postgres',
    'password': 'your_password'
}

mcp_server = DataEngineeringMCPServer(db_config)
mcp_server.connect()

# Define tools for Claude
tools = [
    {
        "name": "execute_query",
        "description": "Execute a SQL query against the database.",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The SQL query to execute."
                }
            },
            "required": ["query"]
        }
    },
    {
        "name": "get_schema",
        "description": "Fetch the schema for a table.",
        "input_schema": {
            "type": "object",
            "properties": {
                "table_name": {
                    "type": "string",
                    "description": "The name of the table."
                }
            },
            "required": ["table_name"]
        }
    },
    {
        "name": "validate_data",
        "description": "Run data quality checks on a table.",
        "input_schema": {
            "type": "object",
            "properties": {
                "table_name": {
                    "type": "string",
                    "description": "The name of the table to validate."
                }
            },
            "required": ["table_name"]
        }
    }
]

def run_agent(task: str) -> str:
    """Run the data engineering agent for a given task."""
    messages = [
        {
            "role": "user",
            "content": task
        }
    ]
    
    while True:
        # Call Claude with tools
        response = client.messages.create(
            model="claude-opus-4-7",
            max_tokens=4096,
            tools=tools,
            messages=messages
        )
        
        # Check if Claude is done
        if response.stop_reason == "end_turn":
            # Extract final text response
            for block in response.content:
                if hasattr(block, 'text'):
                    return block.text
            break
        
        # Process tool calls
        if response.stop_reason == "tool_use":
            # Add assistant's response to messages
            messages.append({"role": "assistant", "content": response.content})
            
            # Process each tool call
            tool_results = []
            for block in response.content:
                if block.type == "tool_use":
                    tool_name = block.name
                    tool_input = block.input
                    
                    # Execute the tool
                    if tool_name == "execute_query":
                        result = mcp_server.execute_query(tool_input["query"])
                    elif tool_name == "get_schema":
                        result = mcp_server.get_schema(tool_input["table_name"])
                    elif tool_name == "validate_data":
                        result = mcp_server.validate_data(tool_input["table_name"])
                    else:
                        result = {"error": f"Unknown tool: {tool_name}"}
                    
                    tool_results.append({
                        "type": "tool_result",
                        "tool_use_id": block.id,
                        "content": json.dumps(result)
                    })
            
            # Add tool results to messages
            messages.append({"role": "user", "content": tool_results})
        else:
            # Unexpected stop reason
            break
    
    return "Agent completed without final response."

# Example usage
if __name__ == "__main__":
    task = """
    I have a table called 'users' with columns user_id, email, created_at, and status.
    1. Fetch the schema for the users table.
    2. Run a validation check to count total rows and nulls per column.
    3. Find all users created in the last 7 days.
    4. Summarize the results.
    """
    
    result = run_agent(task)
    print("Agent Result:")
    print(result)

This agent loop is the core pattern: Claude calls tools, you execute them, and Claude reasons about the results. The Claude 3.5 Sonnet and Claude Code announcement covers the latest capabilities for this kind of agentic work.

Real-World Example: Multi-Step Data Pipeline

Let’s walk through a realistic scenario: ingesting raw customer data, transforming it, and validating the output.

The Task:

You receive a CSV file with customer orders. You need to:

1. Load the CSV into a staging table.
2. Transform it (clean email addresses, standardize dates, deduplicate).
3. Validate the transformed data.
4. Load it into the production analytics table.
5. Report on what was loaded and any issues.

Extending Your Agent:

Add tools for file handling and transformation:

import pandas as pd
import os
from datetime import datetime

class ExtendedMCPServer(DataEngineeringMCPServer):
    def load_csv(self, file_path: str, table_name: str) -> Dict[str, Any]:
        """Load a CSV file into a staging table."""
        try:
            df = pd.read_csv(file_path)
            # Create table from DataFrame
            from sqlalchemy import create_engine
            engine = create_engine(
                f"postgresql://{self.db_config['user']}:{self.db_config['password']}"
                f"@{self.db_config['host']}/{self.db_config['database']}"
            )
            df.to_sql(table_name, engine, if_exists='replace', index=False)
            return {
                'status': 'success',
                'rows_loaded': len(df),
                'columns': list(df.columns)
            }
        except Exception as e:
            return {'status': 'error', 'message': str(e)}
    
    def transform_data(self, source_table: str, target_table: str, 
                      transformations: Dict[str, str]) -> Dict[str, Any]:
        """Apply transformations and load into target table."""
        try:
            # Build transformation query
            select_clause = ', '.join(
                f"{expr} as {col}" for col, expr in transformations.items()
            )
            query = f"CREATE TABLE {target_table} AS SELECT {select_clause} FROM {source_table}"
            result = self.execute_query(query)
            return result
        except Exception as e:
            return {'status': 'error', 'message': str(e)}
    
    def get_data_sample(self, table_name: str, limit: int = 5) -> Dict[str, Any]:
        """Fetch sample rows from a table."""
        query = f"SELECT * FROM {table_name} LIMIT {limit}"
        return self.execute_query(query)

The Agent Prompt:

task = """
You are a data engineering agent. Your job is to:

1. Load the file 'orders.csv' into a staging table called 'orders_staging'.
2. Inspect the schema of the staging table.
3. Create a transformation that:
   - Cleans email addresses (lowercase, trim whitespace)
   - Standardizes dates to ISO format
   - Removes duplicate rows based on order_id
4. Load the transformed data into 'orders_production'.
5. Run validation checks on both tables and compare row counts.
6. Report the results, including any data quality issues.

Work step-by-step. If you encounter an error, diagnose it and retry.
"""

result = run_agent(task)
print(result)

When you run this, Claude will:

1. Call load_csv to ingest the file.
2. Call get_schema to understand the data structure.
3. Call transform_data with SQL transformations.
4. Call validate_data on both tables.
5. Call execute_query to compare row counts.
6. Synthesize the results into a human-readable report.

The agent adapts if something fails. If the CSV has unexpected columns, Claude reasons about how to handle them. If a transformation query is invalid, Claude can adjust and retry.

Integrating with Apache Superset and D23

This agent pattern pairs naturally with D23’s managed Apache Superset platform. Here’s why:

D23 provides a hosted, production-grade Superset instance with AI and API integrations built in. Your data engineering agent can feed into D23’s dashboards and self-serve BI layer. For example:

• The agent ingests and transforms raw data, loading clean tables into your data warehouse.
• D23 dashboards query those tables, giving users self-serve analytics without touching the agent.
• D23’s text-to-SQL capability (powered by Claude) lets users ask questions in plain English, which the agent can then validate and optimize.
• D23’s API-first design means your agent can programmatically update dashboards or trigger alerts based on data quality checks.

This is particularly valuable for teams evaluating alternatives to Preset or other managed Superset providers. You get the flexibility of open-source BI plus the AI-powered automation of an intelligent agent, without vendor lock-in.

For embedded analytics use cases—if you’re building a SaaS product and need to embed dashboards or analytics into your application—the agent handles the backend data pipeline while D23 handles the frontend presentation layer. This separation of concerns is cleaner and more scalable than monolithic BI platforms.

Error Handling and Resilience

Production agents need robust error handling. Claude is smart, but it’s not infallible.

Implement retry logic:

def run_agent_with_retry(task: str, max_retries: int = 3) -> str:
    """Run the agent with retry logic for transient failures."""
    for attempt in range(max_retries):
        try:
            return run_agent(task)
        except Exception as e:
            if attempt < max_retries - 1:
                print(f"Attempt {attempt + 1} failed: {e}. Retrying...")
                continue
            else:
                return f"Agent failed after {max_retries} attempts: {e}"

Add logging for debugging:

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def run_agent_with_logging(task: str) -> str:
    """Run the agent with detailed logging."""
    logger.info(f"Starting agent task: {task}")
    messages = [{"role": "user", "content": task}]
    
    while True:
        response = client.messages.create(
            model="claude-opus-4-7",
            max_tokens=4096,
            tools=tools,
            messages=messages
        )
        
        logger.info(f"Claude response: {response.stop_reason}")
        
        # ... rest of agent loop ...

Set timeouts for tool execution:

import signal

def timeout_handler(signum, frame):
    raise TimeoutError("Tool execution timed out")

def execute_tool_with_timeout(tool_name: str, tool_input: Dict, timeout: int = 30) -> Dict:
    """Execute a tool with a timeout."""
    signal.signal(signal.SIGALRM, timeout_handler)
    signal.alarm(timeout)
    try:
        if tool_name == "execute_query":
            result = mcp_server.execute_query(tool_input["query"])
        # ... other tools ...
        signal.alarm(0)  # Cancel the alarm
        return result
    except TimeoutError:
        return {"status": "error", "message": "Tool execution timed out"}

Advanced: Agentic Reasoning with Reflection

For complex data pipelines, you can add a reflection loop where Claude evaluates its own work.

Add a reflection step:

def run_agent_with_reflection(task: str) -> str:
    """Run the agent and have Claude reflect on the results."""
    # First, run the agent normally
    result = run_agent(task)
    
    # Then, ask Claude to reflect
    reflection_task = f"""
    You just completed a data engineering task. Here's what you did:
    
    {result}
    
    Now, reflect on the work:
    1. Did you accomplish the goal?
    2. Were there any issues or assumptions you made?
    3. What would you do differently next time?
    4. Are there any data quality concerns you should flag?
    """
    
    reflection = run_agent(reflection_task)
    return f"Initial Result:\n{result}\n\nReflection:\n{reflection}"

This pattern is useful when you want the agent to double-check its work or identify potential issues before handing results to downstream systems.

Monitoring and Observability

In production, you need visibility into agent behavior. Track:

• Tool call latency: How long does each tool take?
• Tool failure rates: Which tools fail most often?
• Agent success rate: How often does the agent complete tasks successfully?
• Token usage: How many tokens does each task consume?

import time
from collections import defaultdict

class AgentMetrics:
    def __init__(self):
        self.tool_calls = defaultdict(list)
        self.agent_runs = []
    
    def record_tool_call(self, tool_name: str, duration: float, success: bool):
        self.tool_calls[tool_name].append({
            'duration': duration,
            'success': success,
            'timestamp': datetime.now()
        })
    
    def record_agent_run(self, task: str, duration: float, success: bool, tokens_used: int):
        self.agent_runs.append({
            'task': task,
            'duration': duration,
            'success': success,
            'tokens_used': tokens_used,
            'timestamp': datetime.now()
        })
    
    def get_summary(self):
        return {
            'total_runs': len(self.agent_runs),
            'success_rate': sum(1 for r in self.agent_runs if r['success']) / len(self.agent_runs),
            'avg_duration': sum(r['duration'] for r in self.agent_runs) / len(self.agent_runs),
            'total_tokens': sum(r['tokens_used'] for r in self.agent_runs),
            'tool_stats': {
                name: {
                    'calls': len(calls),
                    'success_rate': sum(1 for c in calls if c['success']) / len(calls),
                    'avg_duration': sum(c['duration'] for c in calls) / len(calls)
                }
                for name, calls in self.tool_calls.items()
            }
        }

metrics = AgentMetrics()

This data is valuable for optimizing your agent and understanding where bottlenecks occur.

Scaling Your Agent

As your agent handles more tasks, consider:

Parallelization: If you have multiple independent tasks, run them concurrently.

import concurrent.futures

def run_agents_parallel(tasks: List[str]) -> List[str]:
    """Run multiple agent tasks in parallel."""
    with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
        futures = [executor.submit(run_agent, task) for task in tasks]
        return [f.result() for f in concurrent.futures.as_completed(futures)]

Caching: Store results of expensive operations (schema queries, data samples) to avoid redundant calls.

from functools import lru_cache

@lru_cache(maxsize=128)
def cached_get_schema(table_name: str):
    return mcp_server.get_schema(table_name)

Batching: Group related tasks to reduce overhead.

def run_batch_validation(table_names: List[str]) -> Dict[str, Any]:
    """Validate multiple tables in a single agent run."""
    task = f"Validate these tables and report issues: {', '.join(table_names)}"
    return run_agent(task)

Comparing to Traditional Approaches

How does this agent-based approach compare to Airflow, dbt, or managed BI platforms?

vs. Airflow: Airflow excels at orchestration and scheduling. An agent complements Airflow by adding reasoning and adaptability. You might use Airflow to schedule agent runs on a cadence, with the agent handling the actual data work.

vs. dbt: dbt is a transformation framework focused on SQL. An agent can call dbt (via a tool) and reason about when and how to run it. Agents add a reasoning layer on top of dbt.

vs. Looker/Tableau/Power BI: These are presentation layers. An agent handles the backend data pipeline. Together with a tool like D23, which provides managed Superset hosting with API and AI integrations, you get a complete stack: agent for data engineering, Superset for analytics, and AI for natural-language queries.

vs. Preset: Preset is a managed Superset offering. D23 is a modern alternative built on Apache Superset with native AI and API-first design. An agent fits naturally into the D23 ecosystem, automating the data pipeline that feeds your dashboards.

The key advantage of the agent approach is flexibility and reasoning. Traditional tools follow rigid workflows. Agents adapt to unexpected data, diagnose issues, and make decisions based on context.

Best Practices for Production Agents

1. Start simple: Build a minimal agent for a single task, then expand.
2. Test thoroughly: Unit test individual tools before integrating them into the agent.
3. Monitor closely: Log every agent run and tool call. Set up alerts for failures.
4. Version your tools: As you update tools, version them so agents can specify which version to use.
5. Implement guardrails: Restrict agent capabilities (e.g., read-only access to production databases) until you’re confident.
6. Document assumptions: Make explicit what the agent is allowed to do and not do.
7. Use structured outputs: Have Claude return results in JSON format for easier downstream processing.

Learning More

For deeper dives into agentic systems, check out these resources:

• The LLM Agents: A Comprehensive Introduction from Hugging Face covers foundational concepts.
• Building AI Agents with LLMs: A Practical Guide on Towards Data Science walks through real-world patterns.
• The DataCamp tutorial on building AI agents with LLMs includes hands-on examples.
• LangChain Agents Documentation and the LangChain Python Agents Guide provide frameworks and abstractions.

For Claude-specific guidance, the Anthropic Claude Documentation covers tool calling and agentic patterns in detail.

Conclusion

Building a data engineering agent with Claude Opus 4.7 and MCP is practical and powerful. You get a system that ingests, transforms, and validates data with reasoning and adaptability that traditional ETL pipelines lack.

The pattern is straightforward: define tools via MCP, let Claude call them, and iterate based on results. Start with a simple agent for one task, then expand as you gain confidence.

For teams building on Apache Superset or evaluating managed BI alternatives, this agent approach is a natural fit. It automates the backend data pipeline while keeping your frontend analytics flexible and open. When paired with a platform like D23—which offers managed Superset hosting with native AI and API integrations—you get a complete, modern analytics stack without the platform overhead of Looker, Tableau, or Power BI.

The future of data engineering is less about rigid pipelines and more about intelligent systems that reason about data, adapt to change, and explain their decisions. Start building your agent today.