> ## Documentation Index
> Fetch the complete documentation index at: https://pavan-c90e8846.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# MCP Agents: Model Context Protocol Examples

> Discover 13 MCP projects that connect agents to external tools via the Model Context Protocol, integrating GitHub, databases, docs, and more.

The MCP Agents category contains 13 projects that demonstrate how to connect LLM agents to external tools and services using the Model Context Protocol (MCP). MCP provides a standardized way for agents to discover and invoke external functionality, such as querying databases, reading GitHub repositories, searching documentation, and interacting with productivity tools.

## MCP Agent Projects

| Project | Integration | Description |
| - | - | - |
| Doc-MCP | Docs, RAG | Semantic RAG documentation and Q\&A system |
| LangGraph MCP Agent | Couchbase | LangChain ReAct agent with Couchbase integration |
| GitHub MCP Agent | GitHub | Repository insights and analysis via MCP |
| MCP Starter | GitHub | GitHub repository analyzer starter template |
| Talk to your Docs | Docs Q\&A | Documentation Q\&A agent with MCP |
| Database MCP Agent | GibsonAI | Conversational AI agent for managing database projects and schemas |
| Hotel Finder Agent | Hotel API | Hotel search and booking using MCP integration |
| Custom MCP Server | Custom | Custom MCP server implementation example |
| Couchbase MCP Server | Couchbase | Couchbase database integration with MCP protocol |
| ScaleKit Exa MCP Security | Security | Security-focused MCP integration with Exa search |
| Docker E2B MCP Agent | Docker, E2B | Secure AI agent running in sandboxed Docker environments via MCP Gateway |
| Taskade MCP Agent | Taskade | Workspace agent for managing projects, tasks, and workflows via Taskade MCP |
| Telemetry MCP Okahu | Okahu Cloud | Self-healing Text-to-SQL demo using Okahu Cloud traces via hosted MCP |

## Running an MCP Agent

Most MCP projects use a client-server architecture. The agent (client) connects to an MCP server that exposes tools the agent can call.

<Steps>
  <Step title="Navigate into the project">
    ```bash theme={null}
    cd mcp_ai_agents/<project_name>
    ```
  </Step>

  <Step title="Configure environment keys">
    ```bash theme={null}
    cp .env.example .env
    ```

    MCP projects usually require keys for both the LLM provider and the external service (e.g., GitHub token, Couchbase credentials, Exa API).
  </Step>

  <Step title="Install and run">
    ```bash theme={null}
    pip install -r requirements.txt
    python main.py
    ```

    Some projects also require starting the MCP server separately. Check the README for those instructions.
  </Step>
</Steps>

<Tip>
  If you are new to MCP, start with the **MCP Starter** or **Talk to your Docs** projects. They clearly demonstrate how an agent discovers and calls tools through the MCP layer.
</Tip>


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