MCP Servers and CLI Tools: Building the AI-Developer Bridge
The biggest limitation of AI coding assistants has always been context. They can write code, but they can’t see your project. They can suggest solutions, but they can’t verify them against your actual database schema. That’s changing with MCP — the Model Context Protocol — and as Myles Ndlovu, I’ve been building with it extensively. Here’s what developers need to know.
What MCP Actually Is
MCP (Model Context Protocol) is a standardised way for AI models to interact with external tools, data sources, and systems. Think of it as an API layer between an LLM and the real world. Instead of describing your database schema to the AI, the AI can query your database directly. Instead of pasting error logs into a chat, the AI can read your monitoring dashboard.
The protocol defines a simple interface: tools (actions the AI can take), resources (data the AI can read), and prompts (templates for common interactions). Any developer can build an MCP server that exposes their own tools through this interface.
Why This Matters for Developer Productivity
Before MCP, working with AI felt like explaining a codebase to a consultant over the phone. You’d describe your file structure, paste in relevant code, explain the relationships between components — and then hope the AI understood enough context to give useful advice.
With MCP servers connected, the AI has direct access to your codebase, your git history, your test results, your deployment logs, and anything else you choose to expose. The quality of assistance jumps dramatically because the AI is working with reality, not your description of reality.
In my own workflow, MCP integration reduced the time I spend providing context to AI tools by roughly 70%. Instead of carefully crafting prompts with code snippets, I describe what I want and the AI gathers the context it needs.
Building Your First MCP Server
An MCP server is surprisingly simple to build. At its core, it’s a JSON-RPC server that responds to three types of requests: listing available tools, listing available resources, and executing tool calls.
Here’s the mental model: you define tools that represent actions (deploy to staging, run tests, query the database), and resources that represent read-only data (project structure, configuration files, recent logs). The AI model decides when to use which tool based on the conversation context.
For a typical web project, I build MCP servers that expose: database schema inspection, recent error logs from production, test suite execution, deployment status, and API documentation. Each of these eliminates a round-trip where I’d previously have to manually gather information and paste it into the AI chat.
Practical Examples
Database MCP Server. Exposes your database schema, lets the AI run read-only queries, and provides table relationship information. When I ask “what’s the average response time for API calls this week,” the AI can query the metrics table directly instead of asking me to run the query and paste the results.
Deployment MCP Server. Connects to your CI/CD pipeline. The AI can check deployment status, read recent deploy logs, and even trigger deployments to staging environments. When something breaks after a deploy, the AI can correlate the timing with specific code changes without me manually checking git logs.
Monitoring MCP Server. Reads from your application’s health endpoints, log aggregation service, and error tracking system. The AI can proactively identify patterns in errors and suggest fixes based on the actual error data, not a description of it.
CLI Tools That Understand Natural Language
The second major shift is in CLI tools. Traditional CLIs require you to memorise exact commands, flags, and argument formats. AI-powered CLIs let you describe what you want in plain language.
I’ve built internal tools that wrap our infrastructure management in a conversational interface. New developers don’t need to learn kubectl get pods -n production -l app=api --field-selector status.phase=Running — they type “show running API pods in production” and the tool generates and executes the right command.
The key to building effective AI CLI tools is constraining the action space. You don’t want the AI to have unrestricted access to your infrastructure. Define a clear set of allowed actions, implement confirmation prompts for destructive operations, and log everything. The AI suggests; the human confirms.
Security Considerations
MCP servers that connect AI models to production systems need careful security design. Here are the principles I follow:
Read-only by default. Every MCP tool should be read-only unless write access is explicitly required and approved. The AI should be able to inspect your database schema but not modify tables.
Scoped access. Don’t expose your entire infrastructure. Build separate MCP servers for different concerns — one for database, one for deployments, one for monitoring — and connect only what’s needed for the current task.
Audit logging. Every tool call made by the AI should be logged with full context: what was requested, what was returned, and the conversation context that triggered it. This isn’t just for security — it’s invaluable for understanding how AI tools interact with your systems.
No secrets in context. MCP responses should never include credentials, API keys, or personally identifiable information. Sanitise output before it reaches the model.
The Future of AI-Assisted Development
MCP represents a fundamental shift in how developers interact with AI. We’re moving from “AI as a chat partner” to “AI as an integrated development partner.” The difference is context — and context is everything.
The developers who invest time in building MCP integrations for their specific tools and workflows will have a significant productivity advantage. It’s not about using a better AI model — it’s about giving whatever model you use better access to the information it needs to help you effectively.
The bridge between AI and real-world development isn’t theoretical anymore. It’s here, it’s practical, and it’s transforming how productive developers can be. The only question is whether you build the bridge or wait for someone else to build it for you.
Myles Ndlovu builds algorithmic trading engines, crypto platforms, and payment infrastructure for emerging markets. Read more about Myles or get in touch.