init

functionality/mcp/README.md

@@ -0,0 +1,61 @@
+# MCP in AgentScope
+
+This example demonstrates how to
+
+- create MCP client with different transports (SSE and Streamable HTTP) and type (Stateless and Stateful),
+- register MCP tool functions and use them in a ReAct agent, and
+- get MCP tool function as a local callable object from the MCP client.
+
+
+## Prerequisites
+
+- Python 3.10 or higher
+- DashScope API key from Alibaba Cloud
+
+## Installation
+
+### Install from PyPI (Recommended)
+
+### Install AgentScope
+
+```bash
+# Install from source
+cd {PATH_TO_AGENTSCOPE}
+pip install -e .
+```
+
+## QuickStart
+
+Install agentscope and ensure you have a valid DashScope API key in your environment variables.
+
+> Note: The example is built with DashScope chat model. If you want to change the model in this example, don't forget
+> to change the formatter at the same time! The corresponding relationship between built-in models and formatters are
+> list in [our tutorial](https://doc.agentscope.io/tutorial/task_prompt.html#id1)
+
+```bash
+pip install agentscope
+```
+
+Start the MCP servers by the following commands in two separate terminals:
+
+```bash
+# In one terminal, run:
+python mcp_add.py
+
+# In another terminal, run:
+python mcp_multiply.py
+```
+
+Two MCP servers will be started on `http://127.0.0.1:8001` (SSE server) and `http://127.0.0.1:8002` (streamable
+HTTP server).
+
+After starting the MCP servers, you can run the agent example:
+
+```bash
+python main.py
+```
+
+The agent will:
+1. Register the MCP tools from the servers
+2. Use a ReAct agent to solve a calculation problem (multiplying two numbers and then adding another number)
+3. Return structured output with the final result