Built With
AI Agents Built With MCP (Model Context Protocol)
MCP is like USB for AI agents — a universal standard for connecting models to external tools and data. Instead of building custom integrations for every tool, you connect through MCP once. Built by Anthropic, adopted across the ecosystem. As the library of MCP servers grows, your agent's capabilities grow with it — zero additional development.
The Technology
Why I Use MCP (Model Context Protocol)
Before MCP, connecting an AI agent to a new tool meant building a custom integration: reading API docs, writing wrapper functions, handling auth, managing errors. Repeat for every tool. Connect to 10 tools, build 10 integrations. Switch LLM providers, rebuild them all.
MCP changes this equation. It's a standardized protocol for agent-to-tool communication. Your agent connects to MCP servers that expose tool capabilities. Need database access? There's an MCP server for PostgreSQL. Need file system access? There's one for that. GitHub, Slack, Google Drive — the ecosystem is growing fast. Your agent discovers available tools at runtime and uses them through a consistent interface.
The security model is a key differentiator. MCP servers define what operations are available and what data is accessible, with controlled scopes and permission management. This is dramatically safer than giving an agent raw API keys to every service. I use MCP increasingly in client deployments because it reduces integration development time by 50-70% and provides a cleaner security boundary between the agent and its tools.
Capabilities
What MCP (Model Context Protocol) Enables
Standardized protocol connecting AI agents to any external data source, tool, or API
Built-in security model with controlled access scopes, permissions, and audit capability
Support for local MCP servers (your infrastructure) and remote hosted servers
Growing ecosystem of pre-built servers: GitHub, Slack, databases, file systems, and more
Resource discovery: agents dynamically find and use available tools at runtime
Bidirectional communication: tools provide context and receive instructions from agents
In Practice
How I Use MCP (Model Context Protocol) in Agent Systems
MCP-connected agents access tools through a standardized protocol instead of custom integrations. The agent discovers available MCP servers, queries their capabilities, and uses them with consistent authentication and error handling. Adding a new tool is adding an MCP server — not writing custom integration code. Security scopes control exactly what the agent can access through each server.
Use Cases
MCP (Model Context Protocol) in Action
Connecting agents to databases, file systems, and internal APIs through one protocol
Enterprise agents with secure, governed access to company resources
Portable agent tools that work across different LLM providers and frameworks
AI coding assistants with access to repos, docs, and deployment systems
Agents that dynamically discover and use new tools as they're added to the ecosystem
FAQ
MCP (Model Context Protocol) Questions
Is MCP only for Claude/Anthropic models?
No. MCP is an open standard. While Anthropic created it and Claude has native support, MCP servers work with any model through MCP client libraries. The protocol is model-agnostic — it defines how tools expose capabilities, not which model consumes them.
How mature is the MCP ecosystem?
Growing fast. As of early 2026, there are pre-built MCP servers for PostgreSQL, SQLite, GitHub, Slack, Google Drive, file systems, web scraping, and dozens more. New servers appear weekly. For tools without existing servers, building a custom MCP server takes 2-4 hours — much less than building a full custom integration.
Is MCP secure for enterprise use?
Yes. MCP includes a security model with scope definitions, permission controls, and audit logging. Each server defines exactly what operations are available. The agent can only do what the server exposes — no raw API access. This is more secure than giving agents API keys directly, because the MCP server acts as a controlled gateway.
How does MCP compare to just building custom tool functions?
Custom functions work fine for 3-5 tools. At 10+ tools, the maintenance burden grows and every provider switch means rebuilding. MCP gives you a consistent interface, automatic tool discovery, and security controls that custom functions lack. The standardization also means your tools are portable across agent frameworks.
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