How to Use the DeepSource MCP in CrewAI
Deploy a team of CrewAI agents to monitor DeepSource code quality and auto-assign security vulnerabilities.
Works with every AI agent you already use
…and any MCP-compatible client
Connect DeepSource MCP to CrewAI
Create your Vinkius account to connect DeepSource to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Run autonomous security audits with CrewAI
The `list_vulnerabilities` tool fetches all security risks from your repository dependencies. In a CrewAI setup, a Security Agent analyzes this list to prioritize critical CVSS scores and identify reachable packages. The Security Agent then hands the data to a Remediation Agent, which calls `get_vulnerability` to retrieve specific details on the vulnerable package. The team then drafts a pull request to update the dependency without human intervention.
Monitor codebase health with specialized agents
The `get_report_card` tool tracks the overall grade of your codebase. A Quality Monitor Agent runs this tool daily to watch for any grade drops on your default branch. If the grade drops, the Monitor Agent alerts an Analyst Agent, which calls `list_issues` to find new code smells or anti-patterns. The Analyst Agent groups these issues by file and assigns them to the responsible team members.
Automate repo maintenance using this MCP Server
The `get_repository` tool checks if your repository configurations match company standards. A Compliance Agent queries this tool to verify repository owners, VCS settings, and active branches. If a repository is unmonitored, the Compliance Agent runs `activate_repository` to enable analysis. If the default branch changes in your VCS, the agent calls `update_default_branch` to keep DeepSource aligned.
Set up DeepSource MCP in CrewAI
Prerequisites
- Python 3.10+ installed
-
crewaipackage (pip install crewai) - Active Vinkius subscription with a valid endpoint token
- 1
Install CrewAI
Run
pip install crewaito install the framework. MCP support is built-in via themcpsparameter. - 2
Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the
mcpslist. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically. - 3
Kick off your crew
Create a
Crewwith your agent and tasks. Callcrew.kickoff()— the agent will automatically invoke DeepSource tools as needed.
from crewai import Agent, Task, Crew
agent = Agent(
role="DeepSource Analyst",
goal="Access and analyze DeepSource data via MCP.",
backstory="Expert analyst with direct DeepSource access.",
mcps=[
"https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
],
)
task = Task(
description="List recent DeepSource transactions",
agent=agent,
expected_output="A summary of recent activity",
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result) Prerequisites
- Python 3.10+ installed
-
crewai+crewai-toolspackages - Active Vinkius subscription with a valid endpoint token
- 1
Install dependencies
Run
pip install crewai crewai-tools. TheMCPServerAdapterhandles lifecycle management and tool conversion. - 2
Connect with MCPServerAdapter
Use
MCPServerAdapteras a context manager withSseServerParameterspointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle. - 3
Assign tools and run
Pass the returned
mcp_toolsto your agent'stoolsparameter. The adapter converts MCP tools to nativeBaseToolobjects compatible with all CrewAI agents.
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters
server_params = SseServerParameters(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
with MCPServerAdapter(server_params) as mcp_tools:
agent = Agent(
role="DeepSource Analyst",
goal="Access and analyze DeepSource data via MCP.",
backstory="Expert analyst with direct DeepSource access.",
tools=mcp_tools,
)
task = Task(
description="List recent DeepSource transactions",
agent=agent,
expected_output="A summary of recent activity",
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DeepSource. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
Why Choose Vinkius
Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.
Real-time monitoring
Live
visibility into every interaction
Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
Built-in savings
60%
lower AI costs
Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.
Single dashboard
One
place for every integration
Every tool your AI connects to, managed from a single screen. One account, complete control.
Common questions about DeepSource MCP in CrewAI
Use it with your favorite AI tools
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