---
title: MCP DSL Integration
description: Learn how to use CrewAI's simple DSL syntax to integrate MCP servers directly with your agents using the mcps field.
icon: code
mode: "wide"
---

## Overview

CrewAI's MCP DSL (Domain Specific Language) integration provides the **simplest way** to connect your agents to MCP (Model Context Protocol) servers. Just add an `mcps` field to your agent and CrewAI handles all the complexity automatically.

<Info>
  This is the **recommended approach** for most MCP use cases. For advanced
  scenarios requiring manual connection management, see
  [MCPServerAdapter](/en/mcp/overview#advanced-mcpserveradapter).
</Info>

## Basic Usage

Add MCP servers to your agent using the `mcps` field:

```python
from crewai import Agent

agent = Agent(
    role="Research Assistant",
    goal="Help with research and analysis tasks",
    backstory="Expert assistant with access to advanced research tools",
    mcps=[
        "https://mcp.exa.ai/mcp?api_key=your_key&profile=research"
    ]
)

# MCP tools are now automatically available!
# No need for manual connection management or tool configuration
```

## Supported Reference Formats

### External MCP Remote Servers

```python
# Basic HTTPS server
"https://api.example.com/mcp"

# Server with authentication
"https://mcp.exa.ai/mcp?api_key=your_key&profile=your_profile"

# Server with custom path
"https://services.company.com/api/v1/mcp"
```

### Specific Tool Selection

Use the `#` syntax to select specific tools from a server:

```python
# Get only the forecast tool from weather server
"https://weather.api.com/mcp#get_forecast"

# Get only the search tool from Exa
"https://mcp.exa.ai/mcp?api_key=your_key#web_search_exa"
```

### Connected MCP Integrations

Connect MCP servers from the CrewAI catalog or bring your own. Once connected in your account, reference them by slug:

```python
# Connected MCP with all tools
"snowflake"

# Specific tool from a connected MCP
"stripe#list_invoices"

# Multiple connected MCPs
mcps=[
    "snowflake",
    "stripe",
    "github"
]
```

## Complete Example

Here's a complete example using multiple MCP servers:

```python
from crewai import Agent, Task, Crew, Process

# Create agent with multiple MCP sources
multi_source_agent = Agent(
    role="Multi-Source Research Analyst",
    goal="Conduct comprehensive research using multiple data sources",
    backstory="""Expert researcher with access to web search, weather data,
    financial information, and academic research tools""",
    mcps=[
        # External MCP servers
        "https://mcp.exa.ai/mcp?api_key=your_exa_key&profile=research",
        "https://weather.api.com/mcp#get_current_conditions",

        # Connected MCPs from catalog
        "snowflake",
        "stripe#list_invoices",
        "github#search_repositories"
    ]
)

# Create comprehensive research task
research_task = Task(
    description="""Research the impact of AI agents on business productivity.
    Include current weather impacts on remote work, financial market trends,
    and recent academic publications on AI agent frameworks.""",
    expected_output="""Comprehensive report covering:
    1. AI agent business impact analysis
    2. Weather considerations for remote work
    3. Financial market trends related to AI
    4. Academic research citations and insights
    5. Competitive landscape analysis""",
    agent=multi_source_agent
)

# Create and execute crew
research_crew = Crew(
    agents=[multi_source_agent],
    tasks=[research_task],
    process=Process.sequential,
    verbose=True
)

result = research_crew.kickoff()
print(f"Research completed with {len(multi_source_agent.mcps)} MCP data sources")
```

## Tool Naming and Organization

CrewAI automatically handles tool naming to prevent conflicts:

```python
# Original MCP server has tools: "search", "analyze"
# CrewAI creates tools: "mcp_exa_ai_search", "mcp_exa_ai_analyze"

agent = Agent(
    role="Tool Organization Demo",
    goal="Show how tool naming works",
    backstory="Demonstrates automatic tool organization",
    mcps=[
        "https://mcp.exa.ai/mcp?api_key=key",      # Tools: mcp_exa_ai_*
        "https://weather.service.com/mcp",         # Tools: weather_service_com_*
        "snowflake"                                # Tools: snowflake_*
    ]
)

# Each server's tools get unique prefixes based on the server name
# This prevents naming conflicts between different MCP servers
```

## Error Handling and Resilience

The MCP DSL is designed to be robust and user-friendly:

### Graceful Server Failures

```python
agent = Agent(
    role="Resilient Researcher",
    goal="Research despite server issues",
    backstory="Experienced researcher who adapts to available tools",
    mcps=[
        "https://primary-server.com/mcp",         # Primary data source
        "https://backup-server.com/mcp",          # Backup if primary fails
        "https://unreachable-server.com/mcp",     # Will be skipped with warning
        "snowflake"                               # Connected MCP from catalog
    ]
)

# Agent will:
# 1. Successfully connect to working servers
# 2. Log warnings for failing servers
# 3. Continue with available tools
# 4. Not crash or hang on server failures
```

### Timeout Protection

All MCP operations have built-in timeouts:

- **Connection timeout**: 10 seconds
- **Tool execution timeout**: 30 seconds
- **Discovery timeout**: 15 seconds

```python
# These servers will timeout gracefully if unresponsive
mcps=[
    "https://slow-server.com/mcp",        # Will timeout after 10s if unresponsive
    "https://overloaded-api.com/mcp"      # Will timeout if discovery takes > 15s
]
```

## Performance Features

### Automatic Caching

Tool schemas are cached for 5 minutes to improve performance:

```python
# First agent creation - discovers tools from server
agent1 = Agent(role="First", goal="Test", backstory="Test",
               mcps=["https://api.example.com/mcp"])

# Second agent creation (within 5 minutes) - uses cached tool schemas
agent2 = Agent(role="Second", goal="Test", backstory="Test",
               mcps=["https://api.example.com/mcp"])  # Much faster!
```

### On-Demand Connections

Tool connections are established only when tools are actually used:

```python
# Agent creation is fast - no MCP connections made yet
agent = Agent(
    role="On-Demand Agent",
    goal="Use tools efficiently",
    backstory="Efficient agent that connects only when needed",
    mcps=["https://api.example.com/mcp"]
)

# MCP connection is made only when a tool is actually executed
# This minimizes connection overhead and improves startup performance
```

## Integration with Existing Features

MCP tools work seamlessly with other CrewAI features:

```python
from crewai.tools import BaseTool

class CustomTool(BaseTool):
    name: str = "custom_analysis"
    description: str = "Custom analysis tool"

    def _run(self, **kwargs):
        return "Custom analysis result"

agent = Agent(
    role="Full-Featured Agent",
    goal="Use all available tool types",
    backstory="Agent with comprehensive tool access",

    # All tool types work together
    tools=[CustomTool()],                          # Custom tools
    apps=["gmail", "slack"],                       # Platform integrations
    mcps=[                                         # MCP servers
        "https://mcp.exa.ai/mcp?api_key=key",
        "snowflake"
    ],

    verbose=True,
    max_iter=15
)
```

## Best Practices

### 1. Use Specific Tools When Possible

```python
# Good - only get the tools you need
mcps=["https://weather.api.com/mcp#get_forecast"]

# Less efficient - gets all tools from server
mcps=["https://weather.api.com/mcp"]
```

### 2. Handle Authentication Securely

```python
import os

# Store API keys in environment variables
exa_key = os.getenv("EXA_API_KEY")
exa_profile = os.getenv("EXA_PROFILE")

agent = Agent(
    role="Secure Agent",
    goal="Use MCP tools securely",
    backstory="Security-conscious agent",
    mcps=[f"https://mcp.exa.ai/mcp?api_key={exa_key}&profile={exa_profile}"]
)
```

### 3. Plan for Server Failures

```python
# Always include backup options
mcps=[
    "https://primary-api.com/mcp",       # Primary choice
    "https://backup-api.com/mcp",        # Backup option
    "snowflake"                          # Connected MCP fallback
]
```

### 4. Use Descriptive Agent Roles

```python
agent = Agent(
    role="Weather-Enhanced Market Analyst",
    goal="Analyze markets considering weather impacts",
    backstory="Financial analyst with access to weather data for agricultural market insights",
    mcps=[
        "https://weather.service.com/mcp#get_forecast",
        "stripe#list_invoices"
    ]
)
```

## Troubleshooting

### Common Issues

**No tools discovered:**

```python
# Check your MCP server URL and authentication
# Verify the server is running and accessible
mcps=["https://mcp.example.com/mcp?api_key=valid_key"]
```

**Connection timeouts:**

```python
# Server may be slow or overloaded
# CrewAI will log warnings and continue with other servers
# Check server status or try backup servers
```

**Authentication failures:**

```python
# Verify API keys and credentials
# Check server documentation for required parameters
# Ensure query parameters are properly URL encoded
```

## Advanced: MCPServerAdapter

For complex scenarios requiring manual connection management, use the `MCPServerAdapter` class from `crewai-tools`. Using a Python context manager (`with` statement) is the recommended approach as it automatically handles starting and stopping the connection to the MCP server.