MCP DSL Integration

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.

This is the recommended approach for most MCP use cases. For advanced scenarios requiring manual connection management, see MCPServerAdapter.

Basic Usage

Add MCP servers to your agent using the mcps field:

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

# 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:

# 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:

# 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:

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:

# 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

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

# 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:

# 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:

# 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:

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

# 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

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

# 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

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:

# 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:

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

Authentication failures:

# 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.

​Overview

​Basic Usage

​Supported Reference Formats

​External MCP Remote Servers

​Specific Tool Selection

​Connected MCP Integrations

​Complete Example

​Tool Naming and Organization

​Error Handling and Resilience

​Graceful Server Failures

​Timeout Protection

​Performance Features

​Automatic Caching

​On-Demand Connections

​Integration with Existing Features

​Best Practices

​1. Use Specific Tools When Possible

​2. Handle Authentication Securely

​3. Plan for Server Failures

​4. Use Descriptive Agent Roles

​Troubleshooting

​Common Issues

​Advanced: MCPServerAdapter

Overview

Basic Usage

Supported Reference Formats

External MCP Remote Servers

Specific Tool Selection

Connected MCP Integrations

Complete Example

Tool Naming and Organization

Error Handling and Resilience

Graceful Server Failures

Timeout Protection

Performance Features

Automatic Caching

On-Demand Connections

Integration with Existing Features

Best Practices

1. Use Specific Tools When Possible

2. Handle Authentication Securely

3. Plan for Server Failures

4. Use Descriptive Agent Roles

Troubleshooting

Common Issues

Advanced: MCPServerAdapter