There's a moment when you realize that your AI assistant could be so much more useful if it could just... access things. Your files. Your database. Your project management system. I hit that moment repeatedly while using Claude Desktop, and it drove me to build a suite of MCP servers that finally bridge that gap.
The Challenge
Claude is brilliant at reasoning and writing, but out of the box, it's isolated. It can't see your filesystem. It can't query your database. It can't check your calendar or update your task list. Every interaction requires manual context—copying, pasting, explaining what's in files it can't see.
The Frustration
I'd be deep in a coding session and want Claude to understand my project structure. But that meant manually describing directory layouts, copying file contents, explaining relationships. It felt like trying to collaborate with someone who could only communicate through notes passed under a door.
The Model Context Protocol (MCP) promised a solution—a standardized way for AI assistants to interact with external systems through well-defined tools. But available MCP servers were limited, and most didn't fit my specific needs. I needed to build my own.
My Approach
I approached this as a security challenge first. Giving an AI assistant access to your systems is powerful, but it's also dangerous if done carelessly. Every MCP server I built started with the question: "What's the minimum capability needed, and how do I prevent misuse?"
My design principles:
- Explicit scope: Each server has clearly defined boundaries—specific directories, specific database tables, specific operations
- Input validation everywhere: Never trust what comes from the AI; validate and sanitize all inputs
- Audit logging: Every tool invocation gets logged with full context for troubleshooting and accountability
- Graceful failure: When something goes wrong, fail safely and provide useful error messages
The Solution
I built a suite of MCP servers, each focused on a specific integration need:
File System Server
Gives Claude scoped access to project directories. It can read files, search for content, understand directory structures—but only within paths I've explicitly allowed. Path traversal attacks? Blocked. Access to sensitive directories? Not permitted by design.
Database Server
Connects Claude to PostgreSQL and SQLite databases with read-only access by default. It can explore schemas, run queries, and help with data analysis. Query validation prevents destructive operations, and result limits prevent runaway queries from returning millions of rows.
Project Management Server
Integrates with task tracking systems to let Claude understand project context. It can see what tasks exist, what's in progress, and what's blocked. This context makes code-related conversations much more productive.
The Architecture
Each server follows the same pattern: a thin Python wrapper using the official MCP SDK, with all the security and validation logic in a separate layer. This separation keeps the MCP integration clean while allowing thorough testing of the security boundaries.
Results & Impact
Seamless Integration
Claude now understands my projects, databases, and workflows without manual context sharing.
Security by Design
Every capability is scoped and validated. No accidental exposure of sensitive systems.
Massive Time Savings
No more copy-paste context building. Claude can explore and understand directly.
Better Answers
With real context, Claude's suggestions are more relevant and actionable.
Lessons Learned
- Start restrictive, expand carefully. It's easier to add capabilities than to remove them safely. Begin with minimal permissions and add based on actual need.
- Logging is essential. When the AI does something unexpected, you need to understand what happened. Comprehensive logs have saved me hours of debugging.
- Test with adversarial inputs. Try to break your own tools. What happens if the AI sends malformed requests? What about requests designed to escape boundaries?
- Document for the AI too. Good tool descriptions help the AI use capabilities correctly. Unclear descriptions lead to misuse and frustration.
Want Claude to Understand Your Systems?
I can build custom MCP servers that connect your AI assistant to the specific systems and data sources you need, with security built in from the start.
Let's Build Your Integration