feat: Add MCP client tool

README.md

@@ -52,6 +52,7 @@ Strands Agents Tools provides a powerful set of tools for your agents to use. It
 - ⏱️ **Task Scheduling** - Schedule and manage cron jobs
 - 🧠 **Advanced Reasoning** - Tools for complex thinking and reasoning capabilities
 - 🐝 **Swarm Intelligence** - Coordinate multiple AI agents for parallel problem solving with shared memory
+- 🔌 **MCP Client** - Connect to any Model Context Protocol server and access remote tools
 - 🔄 **Multiple tools in Parallel**  - Call multiple other tools at the same time in parallel with Batch Tool
 
 ## 📦 Installation
@@ -120,6 +121,7 @@ Below is a comprehensive table of all available tools, how to use them with an a
 | stop | `agent.tool.stop(message="Process terminated by user request")` | Gracefully terminate agent execution with custom message |
 | use_llm | `agent.tool.use_llm(prompt="Analyze this data", system_prompt="You are a data analyst")` | Create nested AI loops with customized system prompts for specialized tasks |
 | workflow | `agent.tool.workflow(action="create", name="data_pipeline", steps=[{"tool": "file_read"}, {"tool": "python_repl"}])` | Define, execute, and manage multi-step automated workflows |
+| mcp_client | `agent.tool.mcp_client(action="connect", connection_id="my_server", transport="stdio", command="python", args=["server.py"])` | Connect to any MCP server via stdio, sse, or streamable_http, list tools, and call remote tools with simplified configuration |
 | batch| `agent.tool.batch(invocations=[{"name": "current_time", "arguments": {"timezone": "Europe/London"}}, {"name": "stop", "arguments": {}}])` | Call multiple other tools in parallel. |
 
 ## 💻 Usage Examples
@@ -137,6 +139,63 @@ agent.tool.file_write(path="output.txt", content="Hello, world!")
 agent.tool.editor(command="view", path="script.py")
 ```
 
+### MCP Client Integration
+
+```python
+from strands import Agent
+from strands_tools import mcp_client
+
+agent = Agent(tools=[mcp_client])
+
+# Connect to a custom MCP server via stdio
+agent.tool.mcp_client(
+    action="connect",
+    connection_id="my_tools",
+    transport="stdio",
+    command="python",
+    args=["my_mcp_server.py"]
+)
+
+# List available tools on the server
+tools = agent.tool.mcp_client(
+    action="list_tools",
+    connection_id="my_tools"
+)
+
+# Call a tool from the MCP server
+result = agent.tool.mcp_client(
+    action="call_tool",
+    connection_id="my_tools",
+    tool_name="calculate",
+    tool_args={"x": 10, "y": 20}
+)
+
+# Connect to a SSE-based server
+agent.tool.mcp_client(
+    action="connect",
+    connection_id="web_server",
+    transport="sse",
+    server_url="http://localhost:8080/sse"
+)
+
+# Connect to a streamable HTTP server
+agent.tool.mcp_client(
+    action="connect",
+    connection_id="http_server",
+    transport="streamable_http",
+    server_url="https://api.example.com/mcp",
+    headers={"Authorization": "Bearer token"},
+    timeout=60
+)
+
+# Load MCP tools into agent's registry for direct access
+agent.tool.mcp_client(
+    action="load_tools",
+    connection_id="my_tools"
+)
+# Now you can call MCP tools directly as: agent.tool.mcp_my_tools_calculate(x=10, y=20)
+```
+
 ### Shell Commands
 
 ```python
@@ -425,6 +484,12 @@ The Mem0 Memory Tool supports three different backend configurations:
 |----------------------|-------------|---------|
 | ENV_VARS_MASKED_DEFAULT | Default setting for masking sensitive values | true |
 
+#### MCP Client Tool
+
+| Environment Variable | Description | Default | 
+|----------------------|-------------|---------|
+| STRANDS_MCP_TIMEOUT | Default timeout in seconds for MCP operations | 30.0 |
+
 #### File Read Tool
 
 | Environment Variable | Description | Default |