fe62d04cdb
* fix(oxylabs): report scrape failures instead of raising IndexError
The oxylabs SDK logs HTTP errors and returns an empty response rather than
raising, so the unchecked `response.results[0]` in every Oxylabs tool turned a
rejected request into `IndexError: list index out of range`. Invalid credentials
-- the most likely first-run mistake -- gave no indication of the cause. A
result carrying a non-2xx `status_code` had the same problem one level down: the
job ran, the page did not come back, and the tool returned its empty content as
though the scrape had succeeded, handing the agent "[]".
Both are now reported as a `ToolFailure` naming what went wrong, so the agent
gets something it can act on and the framework records the call as failed:
401 Unauthorized
400 Bad Request - Parameter `parsing_instructions` can be used just with
`parse` parameter set to `true`.
Because the SDK keeps the cause only in its own log, the failing call is run
with a handler attached to the `oxylabs` logger and the status, the API's
explanation and timeouts are read back off it. `code` and `retryable` are set
from the status, so 429 and 5xx are marked worth retrying. Nothing about the
caller's logging configuration is changed; an application that has silenced the
SDK still gets the generic failure.
Content that is neither a string nor a dict is also serialized properly:
`parsing_instructions` commonly yields a list, and the previous `str()`
fallback produced a Python repr with single quotes instead of JSON.
The client construction and response handling these four tools duplicated
verbatim now live in a shared `OxylabsBaseTool`, following the existing
`SerpApiBaseTool` pattern, so the handling above exists in one place. The
generated tool specs change only by the new `locale` field, confirming the
tools' public surface is otherwise untouched.
Also add the `locale` option to the Google Search config, which the docs
already documented but the config model silently dropped, and correct two
copy-paste errors in the docs across all four locales.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(oxylabs): keep concurrent scrape diagnoses apart
The error capture attached a fresh handler to the shared `oxylabs` logger for
each scrape, so two scrapes in flight at once each saw both errors. `_diagnose`
reads the first HTTP status it finds, so a timeout could be reported as the
other request's 400 -- `retryable=False` on a failure that was worth retrying.
One handler now serves every scrape and routes each record to the capture of
the call that caused it via a `ContextVar`, which isolates threads and asyncio
tasks alike. Serializing the captures would have fixed the cross-talk too, but
at the cost of running every scrape one at a time. The handler stays attached
once installed: it is inert outside a capture, and detaching it would race with
concurrent scrapes.
The regression test forces the interleaving -- one capture is held open while
the other call logs -- and fails against the previous implementation.
Also drive `config` through the public constructor in the tests instead of
assigning `__dict__["config"]`, so they would catch `__init__` dropping a
supplied config.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
CrewAI Tools
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description: str = "Detailed description here."
def _run(self, *args, **kwargs):
# Your tool logic here
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@tool("Tool Name")
def my_custom_function(input):
# Tool logic here
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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:
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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(...)
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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(...)
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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()
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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.
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