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# Basic Agent Setup with MCP
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## Overview
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This document shows how to set up a basic Swarms agent with MCP (Model Context Protocol) integration for client-side operations.
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## Basic Agent Setup
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```python
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from swarms import Agent
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from swarms.tools.mcp_integration import MCPServerSseParams
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# Configure MCP server parameters
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mcp_params = MCPServerSseParams(
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url="http://localhost:8081/sse", # MCP server SSE endpoint
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headers={"Accept": "text/event-stream"}, # Required for SSE
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timeout=5.0 # Connection timeout in seconds
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)
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# Initialize agent with MCP configuration
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agent = Agent(
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agent_name="basic_agent", # Name of your agent
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system_prompt="You are a helpful assistant", # Agent's system prompt
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mcp_servers=[mcp_params], # List of MCP server configurations
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max_loops=5, # Maximum number of loops for task execution
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verbose=True # Enable verbose output
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)
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# Run the agent
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result = agent.run("Your task here")
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print(result)
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```
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## Required Parameters
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1. **MCP Server Parameters**:
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- `url`: The SSE endpoint of your MCP server
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- `headers`: Must include `Accept: text/event-stream`
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- `timeout`: Connection timeout in seconds
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2. **Agent Parameters**:
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- `agent_name`: Name of your agent
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- `system_prompt`: Agent's system prompt
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- `mcp_servers`: List of MCP server configurations
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- `max_loops`: Maximum number of loops for task execution
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- `verbose`: Enable verbose output for debugging
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## Example Usage
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```python
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# Create agent
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agent = Agent(
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agent_name="math_agent",
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system_prompt="You are a math assistant",
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mcp_servers=[mcp_params],
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max_loops=5,
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verbose=True
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)
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# Run a math task
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result = agent.run("Add 5 and 3")
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print(result) # Should return 8
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```
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