Secure Tool Use Patterns with Anthropic's Model Context Protocol (MCP)

In the rapidly evolving landscape of custom AI agents development, the fragmentation of integrations has long been a bottleneck. Every time an engineering team wanted an LLM to access a database, a Slack channel, or a GitHub repository, they had to build a custom glue layer. Anthropic’s Model Context Protocol (MCP) changes this paradigm by providing a universal standard for connecting AI systems to data sources and tools.

However, enabling an LLM to execute code, query databases, and modify file systems introduces significant security risks. The "Confused Deputy" problem—where an AI is tricked into performing unauthorized actions by a malicious prompt—becomes a critical vulnerability when agents are given real-world tools.

For CTOs and Senior Engineers, the challenge is no longer just connecting tools, but securing them. This article outlines architectural patterns for implementing secure MCP servers, ensuring that your enterprise AI agents remain robust, compliant, and safe.

Custom AI Agents Development and Deployment Platform

Build, customize, and deploy powerful AI agents tailored to your business needs with 4Geeks AI Agents. Leverage advanced AI technologies for automation, intelligent workflows, customer support bots, data analysis, and more.

Try 4Geeks AI Agents

The MCP Security Model: A Shared Responsibility

MCP operates on a client-host-server architecture. The MCP Host (e.g., Claude Desktop, a custom IDE, or an internal agent platform) connects to MCP Servers (which expose tools and resources). The security boundary lies primarily at the MCP Server.

While the protocol manages the transport (JSON-RPC 2.0 via Stdio or SSE), it does not inherently validate the intent of a tool call. If an LLM requests to DROP TABLE users, a naive MCP server connected to a production database will execute it.

To secure this, we must implement defense-in-depth strategies at the server level.

Pattern 1: The Gatekeeper (Strict Schema Validation)

The first line of defense is strict input validation. Never trust the LLM’s output blindly. MCP servers typically define tools using JSON schemas, but implementation details matter. By using robust data validation libraries like Pydantic in Python, we can enforce strict typing and business logic constraints before any code is executed.

Implementation: Secure Tool Definition

Here is an implementation of a secure tool using the Python mcp SDK (or similar fastmcp abstractions) that enforces strict directory scoping to prevent path traversal attacks—a common vector in custom AI agents development.

from mcp.server.fastmcp import FastMCP
from pydantic import BaseModel, Field, ValidationError
import os
from pathlib import Path

# Initialize the secure server
mcp = FastMCP("SecureFileSystem")

# Define a strict configuration
ALLOWED_ROOT = Path("/var/app/safe_data").resolve()

class ReadFileArgs(BaseModel):
    filepath: str = Field(..., description="Relative path to the file to read.")

    def secure_path(self) -> Path:
        """
        Validates that the requested path is within the ALLOWED_ROOT.
        Prevents Path Traversal (e.g., ../../etc/passwd)
        """
        requested_path = (ALLOWED_ROOT / self.filepath).resolve()
        if not str(requested_path).startswith(str(ALLOWED_ROOT)):
            raise ValueError(f"Access denied: Path {self.filepath} is outside the allowed scope.")
        return requested_path

@mcp.tool()
def safe_read_file(args: ReadFileArgs) -> str:
    """
    Safely reads a file from the sandboxed directory.
    """
    try:
        # Validate and resolve path
        target_path = args.secure_path()
        
        if not target_path.exists():
            return "Error: File not found."
            
        return target_path.read_text()
        
    except ValidationError as e:
        return f"Validation Error: {e}"
    except ValueError as e:
        # Log this security event!
        print(f"SECURITY ALERT: {e}")
        return "Error: Access violation detected."
    except Exception as e:
        return f"System Error: {str(e)}"

if __name__ == "__main__":
    mcp.run()

Key Takeaway: The secure_path method ensures that even if the LLM hallucinates or is injected with a malicious path like ../../etc/passwd, the tool will reject the request before touching the filesystem.

Pattern 2: The Fortress (Containerized Isolation)

Running MCP servers on a developer's local machine or a production server's bare metal is risky. If a tool has a vulnerability (e.g., subprocess.run with unsanitized input), an attacker could gain shell access.

For high-assurance product engineering, MCP servers should be deployed as ephemeral, isolated containers. This limits the "blast radius" of any potential compromise.

Custom AI Agents Development and Deployment Platform

Build, customize, and deploy powerful AI agents tailored to your business needs with 4Geeks AI Agents. Leverage advanced AI technologies for automation, intelligent workflows, customer support bots, data analysis, and more.

Try 4Geeks AI Agents

Docker Compose for MCP

You can deploy your MCP server suite using Docker. This ensures that the agent only has access to the specific network and volume resources defined in the container.

services:
  mcp-postgres-agent:
    build: ./agents/postgres-agent
    environment:
      - DATABASE_URL=postgres://user:pass@db:5432/read_only_replica
    networks:
      - secure_db_net
    deploy:
      resources:
        limits:
          cpus: '0.50'
          memory: 256M
    # Security: Read-only root filesystem
    read_only: true
    volumes:
      - type: tmpfs
        target: /tmp

networks:
  secure_db_net:
    internal: true # No internet access for this agent

By setting internal: true, we ensure the MCP server cannot exfiltrate data to the public internet, a crucial control for enterprise AI compliance.

Pattern 3: The Human Circuit Breaker (Human-in-the-Loop)

For destructive actions (CRUD operations like Create, Update, Delete), algorithmic trust is insufficient. You need a Human-in-the-Loop (HITL) pattern.

While MCP currently focuses on the transport, the implementing client (the "Host") should handle consent. However, the Server can enforce a "Dry Run" mode or require a confirmation token for sensitive operations.

A robust pattern is the Two-Phase Commit for tools:

1. Phase 1: LLM calls plan_delete_resource(id). Server returns a summary and a generated confirmation_token.
2. Phase 2: LLM must present the confirmation_token to execute_delete_resource(id, token).

Ideally, the MCP Host (the UI) intercepts the tool call and presents a "Approve/Reject" dialog to the user before sending the request to the server.

Pattern 4: Principle of Least Privilege (Infrastructure Level)

When configuring Cloud Engineering resources for your MCP servers, avoid using generic "Admin" API keys.

• Database Tools: Create a specific database user with SELECT only permissions for query tools. Create a separate user with write permissions only for specific tables for mutation tools.
• AWS/GCP Credentials: Use IAM roles with granular permissions (e.g., s3:GetObject on a specific bucket only), rather than broad S3FullAccess.

Monitoring and Observability

Security is not a state; it is a process. You must log every tool execution, the arguments provided by the LLM, and the result. This creates an audit trail that is essential for debugging and compliance.

import logging

# Configure structured logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger("audit_log")

@mcp.tool()
def execute_query(query: str):
    logger.info(f"TOOL_EXECUTION: query_db | ARGS: {query} | USER: {get_current_context().user}")
    # ... execution logic ...

Conclusion

The Model Context Protocol represents a massive leap forward in standardizing custom AI agents development. However, the ease of connection should not come at the cost of security. By implementing strict Pydantic validation, containerizing your MCP servers, and adhering to the principle of least privilege, you can build autonomous systems that are both powerful and safe.

At 4Geeks, we specialize in high-end Product Engineering and AI Engineering. We help Global 2000 companies and funded startups build secure, scalable AI infrastructure that leverages cutting-edge protocols like MCP without compromising on security or compliance.

If you are looking to deploy enterprise-grade AI agents, 4Geeks is your partner for secure innovation.

Custom AI Agents Development and Deployment Platform

Build, customize, and deploy powerful AI agents tailored to your business needs with 4Geeks AI Agents. Leverage advanced AI technologies for automation, intelligent workflows, customer support bots, data analysis, and more.

Try 4Geeks AI Agents

FAQs