MCP DSL 集成

了解如何使用 CrewAI 简单的 DSL 语法，通过 mcps 字段将 MCP 服务器直接集成到你的智能体中。

概述

CrewAI 的 MCP DSL（领域特定语言）集成提供了将智能体连接到 MCP（Model Context Protocol）服务器的最简单方式。只需在智能体中添加 mcps 字段，CrewAI 就会自动处理所有复杂性。

INFO

对于大多数 MCP 用例，这是推荐方法。对于需要手动连接管理的高级场景，请参阅 MCPServerAdapter。

基本用法

使用 mcps 字段向你的智能体添加 MCP 服务器：

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

支持的引用格式

外部 MCP 远程服务器

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"

选择特定工具

使用 # 语法从服务器中选择特定工具：

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"

已连接的 MCP 集成

从 CrewAI 目录连接 MCP 服务器，或自带服务器。在账户中连接后，可通过 slug 引用它们：

python

# Connected MCP with all tools
"snowflake"

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

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

完整示例

以下是一个使用多个 MCP 服务器的完整示例：

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")

工具命名与组织

CrewAI 会自动处理工具命名以避免冲突：

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

错误处理与容错

MCP DSL 的设计目标是稳健且用户友好：

优雅处理服务器故障

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

超时保护

所有 MCP 操作都内置超时：

连接超时：10 秒
工具执行超时：30 秒
发现超时：15 秒

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
]

性能特性

自动缓存

工具 schema 会被缓存 5 分钟以提升性能：

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!

按需连接

仅在工具实际使用时才建立工具连接：

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

与现有功能的集成

MCP 工具可与其他 CrewAI 功能无缝协作：

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
)

最佳实践

1. 尽可能使用特定工具

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. 安全地处理身份认证

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. 为服务器故障做好规划

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. 使用描述性的智能体角色

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"
    ]
)

故障排查

常见问题

未发现工具：

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"]

连接超时：

python

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

身份认证失败：

python

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

高级：MCPServerAdapter

对于需要手动连接管理的复杂场景，请使用 crewai-tools 中的 MCPServerAdapter 类。推荐使用 Python 上下文管理器（with 语句），因为它会自动处理与 MCP 服务器连接的启动和停止。

MCP DSL 集成

概述

基本用法

支持的引用格式

外部 MCP 远程服务器

选择特定工具

已连接的 MCP 集成

完整示例

工具命名与组织

错误处理与容错

优雅处理服务器故障

超时保护

性能特性

自动缓存

按需连接

与现有功能的集成

最佳实践

1. 尽可能使用特定工具

2. 安全地处理身份认证

3. 为服务器故障做好规划

4. 使用描述性的智能体角色

故障排查

常见问题

高级：MCPServerAdapter

友情链接