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MCP (Model Context Protocol) agents integrate external tools, databases, and services to extend AI capabilities beyond text generation. These projects demonstrate semantic RAG, database interactions, and real-world tool use.

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What is Model Context Protocol?

Model Context Protocol (MCP) is a standardized way to connect AI agents with external data sources and tools. It enables:

Universal Integration

Connect to databases, APIs, and services through a unified protocol

Tool Discovery

Automatically discover and use available tools and resources

Semantic Context

Provide rich contextual information for better AI responses

Extensibility

Build custom MCP servers for any data source or service

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All MCP Agent Projects

Couchbase MCP Server

OpenAI Agents SDK demo with Couchbase MCP server for NoSQL database operations and vector search.

Database MCP Agent

Database assistant powered by Agno and GibsonAI MCP server for natural language database queries.

Doc MCP

Documentation RAG system with MCP integration for semantic documentation search and retrieval.

GitHub MCP Agent

Repository exploration with natural language queries for issues, PRs, and code quality analysis.

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MCP Integration Patterns

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Server Types

Pre-built MCP servers for common services:
  • @modelcontextprotocol/server-github - GitHub integration
  • GibsonAI MCP servers - Database and data access
  • Custom servers - Build your own for any service
Isolated MCP servers running in containers:
  • Secure code execution
  • Language-specific runtimes
  • Resource isolation
Build specialized servers for:
  • Proprietary APIs
  • Internal databases
  • Legacy systems
  • Custom business logic

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Common Use Cases

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Database Integration

  • Execute SQL queries via natural language
  • Vector similarity search
  • NoSQL document retrieval
  • Real-time data analysis

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External Services

  • GitHub repository management
  • Task management systems
  • Weather and location services
  • Authentication providers

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Document Processing

  • Semantic documentation search
  • RAG with external knowledge bases
  • Multi-source information retrieval

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Technical Architecture

# Typical MCP agent structure
async with MCPServerStdio(
    params={
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-github"],
        "env": {"GITHUB_PERSONAL_ACCESS_TOKEN": token}
    }
) as server:
    agent = Agent(
        mcp_servers=[server],
        model="meta-llama/Meta-Llama-3.1-70B-Instruct",
        instructions="Use GitHub tools to analyze repositories"
    )

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Getting Started

1

Choose an MCP Project

Select a project that matches your integration needs
2

Install MCP Dependencies

Most projects require Node.js and npx for MCP servers
3

Configure Access Tokens

Set up API keys for the services you want to integrate
4

Run MCP Server

The agent will automatically start the MCP server on initialization
5

Interact Naturally

Use natural language to interact with external tools

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Prerequisites

MCP agents typically require:
  • Python 3.10+ for the agent code
  • Node.js and npx for running MCP servers
  • Docker (optional, for container-based servers)
  • Service API keys (GitHub, databases, etc.)

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Next Steps

Add Persistent Memory

Combine MCP with memory for context-aware integrations

Build RAG Systems

Use MCP for document retrieval in RAG workflows

Create Complex Workflows

Integrate MCP tools into multi-agent systems

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