This guide provides the essential code and instructions to get started with MCP for robotics. Using phosphobot and the Model Context Protocol (MCP), you can connect a Large Language Model (LLM) like Claude to a robot, enabling it to access camera feeds and trigger actions through a standardized interface.

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What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open standard that connects Large Language Models to real-world tools and data sources.

Think of it like an USB-C port for AI, a universal translator between an AI and any application.

MCP allows an LLM to “plug into” different systems, giving it the power to see, reason, and, most importantly, act. For MCP robotics, this means giving an AI the hands and eyes to interact with the physical world.

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What are the key concepts of MCP?

  • Tools are real Python functions that the model can call to perform actions.
    • Example: pickup_object(“banana”) to move a robot arm.
  • Resources are read-only data sources, accessible via URIs.
    • Example: file://home/user/notes.txt to expose the content of a local text file.
  • Host / Client / Server architecture
    • Host = the LLM applications that start the connection (e.g. Claude)
    • Client = the MCP protocol that conects tools to the LLM
    • Server = your app exposing tools/resources (e.g. the phosphobot MCP server)
  • Lifespan lets you run startup/shutdown code (e.g., to launch a robot process), and share context across tools.

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Why MCP for Robotics?

Before MCP, connecting an AI to a robot required custom, complex integrations for each specific model and robot. MCP robotics changes this by creating a universal standard.

  • Standardized Control: Any MCP-compatible LLM can control any MCP-enabled robot.
  • Simplified Integration: It removes the need for fragmented, one-off solutions, creating a “plug-and-play” ecosystem for AI and robotics. [14]
  • Real-World Interaction: It bridges the gap between AI’s reasoning capabilities and a robot’s physical actions, enabling tasks like object manipulation based on visual input.

With robots MCP, developers can build powerful applications where an AI can perceive its environment and execute physical tasks.

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How phosphobot Implements MCP Robotics

The phosphobot MCP integration is a practical example of this protocol in action. The basic demo exposes two primary capabilities to the LLM:

  • Camera Stream: A tool that retrieves the current frame from a webcam, giving the LLM vision.
  • Replay Tool: A tool that triggers a pre-recorded robot action, like picking up an object.

The phosphobot MCP server manages these tools and the communication with the robot’s local API.

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Getting Started with phosphobot MCP

Follow these steps to set up your MCP robotics environment.

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Prerequisites

  • Claude for Desktop is installed.
  • Python and git are installed on your system.
  • You are comfortable using a command-line interface.

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Step 1: Install and Run phosphobot

phosphobot is an open-source platform that allows you to control robots, record data, and train robotics AI models.

First, install phosphobot with the command for your OS:

curl -fsSL https://raw.githubusercontent.com/phospho-app/phosphobot/main/install.sh | bash

Next, run the phosphobot server, which will listen for commands from the MCP server.

phosphobot run

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Step 2: Install the phosphobot MCP Server

This server exposes the robot’s controls to Claude. We recommend installing it with uv.

  1. Install uv, a fast Python package installer:
curl -LsSf https://astral.sh/uv/install.sh | sh
  1. Clone the repository and install the server:
# Clone the phospho MCP server repository
git clone https://github.com/phospho-app/phospho-mcp-server.git

# Navigate to the correct directory
cd phospho-mcp-server/phospho-mcp-server

# Install and run the MCP server
uv run mcp install server.py

This command starts the phospho MCP server and registers its tools with Claude. When you open the Claude desktop app, you will see the server and its tools available for use.

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How It Works: Technical Overview

The phosphobot MCP server communicates with the local phosphobot instance via its REST API (defaulting to http://localhost:80).

  • GET /frames: Fetches the latest camera image.
  • POST /recording/play: Executes a pre-recorded robot action.

The PhosphoClient class manages this communication. If you run phosphobot on a port other than 80, you must update the base URL in the tools/phosphobot.py file.

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Testing Your MCP Robotics Setup

You can test the server with the MCP inspector by running:

uv run mcp dev server.py

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Example 1: Using the Robot’s Camera

Ask Claude a question that requires vision:

“What do you see on my desk?”

Claude will use the get_camera_frame tool to answer.

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Example 2: Controlling the Robot’s Actions

Give Claude a command:

“Pick up the banana”

Claude will use the pickup_object tool to perform the action.

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Available Tools

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pickup_object

Triggers a pre-recorded robotic action.

@mcp.tool()
def pickup_object(name: Literal["banana", "black circle", "green cross"]) -> str:
    """Launches a replay episode to simulate picking up a named object."""
    ...

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get_camera_frame

Captures a JPEG image from the phosphobot camera.

@mcp.tool()
def get_camera_frame() -> Image:
    """Captures a JPEG image from phosphobot's camera via the /frames endpoint."""
    ...

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FAQ

Q: What is phosphobot? A: phosphobot is an open-source platform for robotics that helps you control robots, collect data, and train AI models for robotic tasks.

Q: What is phosphobot mcp? A: phosphobot mcp refers to the integration of the phosphobot platform with the Model Context Protocol. It allows an LLM like Claude to control a robot managed by phosphobot by using standardized tools for actions and camera feeds.

Q: Can I use this with a physical robot? A: Yes. phosphobot is designed to control physical robots, allowing you to bridge the gap between AI and hardware.

Q: Can it only use pre-recorded actions? A: No, while the demo uses pre-recorded actions for simplicity, you can extend the phosphobot MCP server to include real-time control commands or trigger any AI model trained with phosphobot (eg: ACT, gr00t).

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Additional Resources