eb281c6d73
InvokeCrewAIAutomationTool required crew_api_url and crew_bearer_token as positional __init__ arguments with no environment-variable fallback, even though docs.crewai.com documents CREWAI_API_URL/CREWAI_BEARER_TOKEN as alternatives. It also could not be instantiated with zero arguments, which blocks CrewAI AMP Studio's "Invoke Amp Automation" internal tool: the Studio runtime resolves tools by class reference and instantiates them with no arguments. - crew_api_url/crew_bearer_token become optional, falling back to CREWAI_API_URL/CREWAI_BEARER_TOKEN (explicit args still win), mirroring the env-var pattern already used by GenerateCrewaiAutomationTool. - crew_name/crew_description become optional too, defaulting to the tool's existing generic name/description, so InvokeCrewAIAutomationTool() never raises at construction time. - Declare env_vars: list[EnvVar] so the tool catalog surfaces the two env vars, matching the sibling tool. - Raise a clear ValueError at use time when crew_api_url/crew_bearer_token are still missing, instead of failing inside `requests` or with the previous confusing TypeError about positional arguments. - Update the tool's README and the edge docs page (docs/edge/en/tools/integration/crewaiautomationtool.mdx) so the "Tool Arguments" table matches the corrected code. Fully backward compatible: existing positional/keyword constructor calls are unchanged. tool.specs.json is left untouched; the "Generate Tool Specifications" CI workflow regenerates and commits it automatically once this is pushed. crewAIInc/crewAI-tools (the previous home of this tool) is archived and can no longer receive pushes, so this fix targets the actively maintained copy of the tool under lib/crewai-tools/ instead.
CrewAI Tools
Empower your CrewAI agents with powerful, customizable tools to elevate their capabilities and tackle sophisticated, real-world tasks.
CrewAI Tools provide the essential functionality to extend your agents, helping you rapidly enhance your automations with reliable, ready-to-use tools or custom-built solutions tailored precisely to your needs.
Quick Links
Homepage | Documentation | Examples | Community
Available Tools
CrewAI provides an extensive collection of powerful tools ready to enhance your agents:
- File Management:
FileReadTool,FileWriteTool - Web Scraping:
ScrapeWebsiteTool,SeleniumScrapingTool - Database Integrations:
MySQLSearchTool - Vector Database Integrations:
MongoDBVectorSearchTool,QdrantVectorSearchTool,WeaviateVectorSearchTool - API Integrations:
SerperApiTool,ExaSearchTool - AI-powered Tools:
DallETool,VisionTool,StagehandTool
And many more robust tools to simplify your agent integrations.
Creating Custom Tools
CrewAI offers two straightforward approaches to creating custom tools:
Subclassing BaseTool
Define your tool by subclassing:
from crewai.tools import BaseTool
class MyCustomTool(BaseTool):
name: str = "Tool Name"
description: str = "Detailed description here."
def _run(self, *args, **kwargs):
# Your tool logic here
Using the tool Decorator
Quickly create lightweight tools using decorators:
from crewai import tool
@tool("Tool Name")
def my_custom_function(input):
# Tool logic here
return output
CrewAI Tools and MCP
CrewAI Tools supports the Model Context Protocol (MCP). It gives you access to thousands of tools from the hundreds of MCP servers out there built by the community.
Before you start using MCP with CrewAI tools, you need to install the mcp extra dependencies:
pip install crewai-tools[mcp]
# or
uv add crewai-tools --extra mcp
To quickly get started with MCP in CrewAI you have 2 options:
Option 1: Fully managed connection
In this scenario we use a contextmanager (with statement) to start and stop the the connection with the MCP server.
This is done in the background and you only get to interact with the CrewAI tools corresponding to the MCP server's tools.
For an STDIO based MCP server:
from mcp import StdioServerParameters
from crewai_tools import MCPServerAdapter
serverparams = StdioServerParameters(
command="uvx",
args=["--quiet", "pubmedmcp@0.1.3"],
env={"UV_PYTHON": "3.12", **os.environ},
)
with MCPServerAdapter(serverparams) as tools:
# tools is now a list of CrewAI Tools matching 1:1 with the MCP server's tools
agent = Agent(..., tools=tools)
task = Task(...)
crew = Crew(..., agents=[agent], tasks=[task])
crew.kickoff(...)
For an SSE based MCP server:
serverparams = {"url": "http://localhost:8000/sse"}
with MCPServerAdapter(serverparams) as tools:
# tools is now a list of CrewAI Tools matching 1:1 with the MCP server's tools
agent = Agent(..., tools=tools)
task = Task(...)
crew = Crew(..., agents=[agent], tasks=[task])
crew.kickoff(...)
Option 2: More control over the MCP connection
If you need more control over the MCP connection, you can instanciate the MCPServerAdapter into an mcp_server_adapter object which can be used to manage the connection with the MCP server and access the available tools.
important: in this case you need to call mcp_server_adapter.stop() to make sure the connection is correctly stopped. We recommend that you use a try ... finally block run to make sure the .stop() is called even in case of errors.
Here is the same example for an STDIO MCP Server:
from mcp import StdioServerParameters
from crewai_tools import MCPServerAdapter
serverparams = StdioServerParameters(
command="uvx",
args=["--quiet", "pubmedmcp@0.1.3"],
env={"UV_PYTHON": "3.12", **os.environ},
)
try:
mcp_server_adapter = MCPServerAdapter(serverparams)
tools = mcp_server_adapter.tools
# tools is now a list of CrewAI Tools matching 1:1 with the MCP server's tools
agent = Agent(..., tools=tools)
task = Task(...)
crew = Crew(..., agents=[agent], tasks=[task])
crew.kickoff(...)
# ** important ** don't forget to stop the connection
finally:
mcp_server_adapter.stop()
And finally the same thing but for an SSE MCP Server:
from mcp import StdioServerParameters
from crewai_tools import MCPServerAdapter
serverparams = {"url": "http://localhost:8000/sse"}
try:
mcp_server_adapter = MCPServerAdapter(serverparams)
tools = mcp_server_adapter.tools
# tools is now a list of CrewAI Tools matching 1:1 with the MCP server's tools
agent = Agent(..., tools=tools)
task = Task(...)
crew = Crew(..., agents=[agent], tasks=[task])
crew.kickoff(...)
# ** important ** don't forget to stop the connection
finally:
mcp_server_adapter.stop()
Considerations & Limitations
Staying Safe with MCP
Always make sure that you trust the MCP Server before using it. Using an STDIO server will execute code on your machine. Using SSE is still not a silver bullet with many injection possible into your application from a malicious MCP server.
Limitations
- At this time we only support tools from MCP Server not other type of primitives like prompts, resources...
- We only return the first text output returned by the MCP Server tool using
.content[0].text
Why Use CrewAI Tools?
- Simplicity & Flexibility: Easy-to-use yet powerful enough for complex workflows.
- Rapid Integration: Seamlessly incorporate external services, APIs, and databases.
- Enterprise Ready: Built for stability, performance, and consistent results.
Contribution Guidelines
We welcome contributions from the community!
- Fork and clone the repository.
- Create a new branch (
git checkout -b feature/my-feature). - Commit your changes (
git commit -m 'Add my feature'). - Push your branch (
git push origin feature/my-feature). - Open a pull request.
Developer Quickstart
pip install crewai[tools]
Development Setup
- Install dependencies:
uv sync - Run tests:
uv run pytest - Run static type checking:
uv run pyright - Set up pre-commit hooks:
pre-commit install
Support and Community
Join our rapidly growing community and receive real-time support:
Build smarter, faster, and more powerful AI solutions—powered by CrewAI Tools.