Unity MCP Integration

<img src="https://r2cdn.perplexity.ai/pplx-full-logo-primary-dark%402x.png" class="logo" width="120"/> # --- # https://modelcontextprotocol.io/introduction https://modelcontextprotocol.io/quickstart/server https://modelcontextprotocol.io/quickstart/client https://modelcontextprotocol.io/docs/concepts/tools give me summary Here is a summary of the provided pages from the Model Context Protocol (MCP) documentation: --- ### **Introduction to MCP** - MCP is an open protocol that standardizes how applications provide context to large language models (LLMs), akin to how USB-C standardizes device connectivity[^1]. - It enables: - Pre-built integrations for LLMs to connect with data and tools. - Flexibility in switching between LLM providers. - Secure data handling within user infrastructure[^1]. - MCP follows a **client-server architecture**: - **MCP Hosts**: Applications like Claude Desktop or IDEs that access data via MCP. - **MCP Clients**: Protocol clients maintaining connections with servers. - **MCP Servers**: Lightweight programs exposing specific capabilities through MCP. - **Local Data Sources** and **Remote Services**: Securely accessed by servers[^1]. --- ### **Quickstart: Server** - This guide demonstrates building an MCP weather server that provides tools for fetching weather forecasts and alerts, connecting it to a host like Claude for Desktop[^2]. - **Core Concepts**: - **Resources**: File-like data accessible by clients. - **Tools**: Functions callable by LLMs with user approval. - **Prompts**: Templates for specific tasks[^2]. - Example implementation includes: - Setting up a Python environment with the `uv` tool. - Using the National Weather Service API to fetch weather data. - Exposing tools like `get-alerts` and `get-forecast` for integration with hosts like Claude for Desktop[^2]. - The server is tested by configuring Claude for Desktop to recognize and interact with it via commands[^2]. --- ### **Quickstart: Client** - This guide explains building an LLM-powered chatbot client capable of connecting to MCP servers[^3]. - Steps include: - Setting up a Python project using `uv`. - Storing API keys securely in environment files. - Creating a client class to manage server connections and process queries[^3]. - Key functionalities: - Connecting to MCP servers via Python or JavaScript scripts. - Handling queries by leveraging available server tools and integrating responses into natural language outputs using LLMs like Claude[^3]. --- ### **Concepts: Tools** - Tools are functions exposed by MCP servers, enabling LLMs to perform specific actions. These tools are defined programmatically and can be invoked securely with user consent[^1]. - Example use cases include automating workflows, accessing external APIs, or interacting with local/remote datasets through standardized interfaces[^1]. --- This documentation provides comprehensive guidance on building both servers and clients using MCP, enabling seamless integration between LLMs and various data sources or tools. <div style="text-align: center">⁂</div> [^1]: https://modelcontextprotocol.io/introduction [^2]: https://modelcontextprotocol.io/quickstart/server [^3]: https://modelcontextprotocol.io/quickstart/client [^4]: https://modelcontextprotocol.io/introduction [^5]: https://modelcontextprotocol.io/quickstart/server [^6]: https://modelcontextprotocol.io/quickstart/client [^7]: https://modelcontextprotocol.io/docs/concepts/tools