mcpField
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MCP Playground

MCP Playground

Local
github.com/skolteDeveloper Tools0Updated 77w ago
Repository

About MCP Playground

MCP Playground is a hands-on project demonstrating an MCP server integrated with AI agents. Its FastAPI backend provides endpoints for retrieving and updating agent context; the React frontend is a placeholder.

Key features

  1. 01FastAPI-based backend
  2. 02Retrieve agent context
  3. 03Update agent context
  4. 04React frontend placeholder

Use cases

  1. 01Experiment with agent context retrieval
  2. 02Update context data for an agent

MCP support

The project describes an MCP server and provides context retrieval and update endpoints; the README does not specify MCP tools or resources.

#mcp#multi-agent#server

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MCP Playground

This project demonstrates a hands-on implementation of a Model Context Protocol (MCP) server integrated with AI agents.

Structure

  • backend: FastAPI-based MCP server
  • frontend: Placeholder for React frontend dashboard

Quickstart

1. Start the backend server
cd backend
pip install -r requirements.txt
uvicorn app.main:app --reload
2. Access context API
  • POST /get_context with JSON { "agent_id": "agent1" }
  • POST /update_context with JSON { "agent_id": "agent1", "context": {"key": "value"} }
3. Frontend setup (TBD)
  • Scaffold React app inside frontend/ using Vite or Create React App.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.