77fb6bdb69
FileReadTool and FileWriterTool assume a durable local disk. That holds on a developer's machine and breaks in any deployment environment where the runtime is ephemeral: whatever an agent writes is discarded when the run ends, and a later run cannot read it back. A crew that generates a report in one task and reads it in the next passes locally and fails there. This adds the seam needed to point those tools at durable storage instead. Both now route every path resolution and every read/write through a FileStore, defaulting to LocalFileStore — the current filesystem behavior, moved rather than rewritten. A deployment registers a different store through register_file_store_factory and the tools pick it up. The store owns its own containment, because the tools call nothing else before doing I/O. For the local store that stays validate_file_path plus the is_relative_to check; another store enforces whatever its own namespace requires, which may be prefix-based rather than realpath-based. resolve() and normalize() are separate so the reader can still pin its declared file for identity without a containment check, and base_dir is anchored through the store so both tools derive the same sandbox root from the same input. open_text() returns a handle rather than a string, which keeps the local store lazy: reading a small window out of a huge file does not pull the whole thing into memory. A store that must fetch eagerly can wrap its payload in StringIO. Behaviour is unchanged: every pre-existing file tool test passes untouched. The new suite stands in a store backed by a dict with no filesystem at all, which is what proves the seam is real — a tool that reached past it to open() or os.path would fail those assertions. It also covers the fallbacks that keep this safe to ship before any integration exists: a factory returning None, and a factory that raises, both leave the local filesystem in place rather than breaking file I/O. No new dependencies. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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.