CrewAI Platform MCP for AI. Orchestrate multi-agent teams with natural conversation.
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CrewAI Platform provides full control over multi-agent workflows. List and manage entire agent teams, start complex automated runs with specific inputs, and track every task in real time from a single chat window.
You can audit agents' roles, view the execution status of active runs, or even manually stop them when they go off track.
What your AI can do
Get inputs
Identify bounded inputs required to kickoff a crew
Get status
Retrieve explicit execution state tracing limits
Kickoff crew
Starts a new multi-agent workflow by accepting a structured JSON payload that defines the initial customer goal or input.
List all deployed workflows and extract pure JSON blueprints that map out every connected agent.
Trigger complex, multi-agent processing immediately using dynamic inputs to start a new workflow run.
Retrieve the current physical state of active workflows, tracking agents as they complete sequential or parallel tasks.
Enumerate all available roles (agents) and modular operations (tasks) to verify their backstories and intended outcomes.
Send instant signals to halt running processes or manage the context boundaries used by the underlying LLMs.
View specific validation criteria for asynchronous results and track where agent outcomes go after standard JSON formatting.
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CrewAI Platform: 10 Tools Available
These tools give your agent client direct access to the core functions of crew management, run monitoring, and task execution across all multi-agent workflows.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using CrewAI Platform on VinkiusGet Inputs
Identify bounded inputs required to kickoff a crew
Get Status
Retrieve explicit execution state tracing limits
Kickoff Crew
Starts a new multi-agent workflow by accepting a structured JSON payload that...
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The CrewAI Platform integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with CrewAI Platform, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CrewAI. 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.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Today, coordinating complex AI tasks means clicking through five different dashboards.
Right now, if your team needs Agent A to gather data and then Agent B to summarize it, you have to manually check multiple status pages. You copy the run ID from Dashboard 1, paste it into Dashboard 2 just to see the current task state, and hope that everything is connected correctly.
With this MCP, you tell your agent client to start the entire process in one conversation. The whole workflow executes autonomously, and you get a single feed of updates on progress and results. It keeps the complexity hidden while keeping you fully informed.
The CrewAI Platform gives you control over the entire lifecycle.
Manual oversight forces you to check if the agents are still alive or if they got stuck on a specific task. You're always worried about whether an agent has completed its backstories correctly before passing it off.
This MCP lets you audit everything, from listing all available teams with `list_crews` to verifying every single role using `get_agent`. You know the process is solid because you can check the structure and the individual components.
What your AI can actually do with this
This MCP lets you orchestrate whole teams of AI agents through natural conversation. Instead of building custom API calls for every step, you define the crew structure—who reports to whom and what their specific roles are. You kick off a complex workflow with just a JSON payload describing the initial problem (like 'Analyze Q3 sales data').
The system handles the handoffs: Agent A writes a report, Agent B critiques it, and Agent C compiles the final summary. If anything breaks or needs changing midway, you can inspect the live run status or even send an interrupt signal to hard-stop the process. Connecting this MCP via Vinkius gives your agent access to the entire catalog of capabilities, making multi-agent orchestration predictable right from your chat client.
019d757e-8204-711d-b0b1-879507ae186e 
Here's how it actually works
The bottom line is: your AI client handles all the token management and state tracking between tools for you.
Subscribe to this MCP, then provide your CrewAI Agent Token found in the platform's dashboard.
Instruct your AI client to start a workflow by referencing an existing crew ID or asking it to create a new run using defined inputs.
Your agent executes the process, and you get back real-time updates on task completion, status changes, and final results.
Who is this actually for?
AI Architects who need to reliably manage complex agent graphs, or Developers tired of manually scripting multi-step processes. If your job involves testing how various AI roles interact with specific data inputs, this is built for you.
Manages and monitors large, complex agent graphs; verifies the detailed role-playing backstories of individual agents.
Triggers autonomous workflows for testing or production use cases, retrieving task results directly into the IDE or chat environment.
Quickly prototypes and tests agentic workflows using only natural language inputs to validate product concepts before engineering work begins.
What Changes When You Connect
Stop guessing if your complex workflow is stuck. Use get_run_status to see the explicit cloud logging and vault limits for active runs, giving you total visibility into execution flow.
Need to start a test run? Instead of writing boilerplate code, use kickoff_crew with a simple JSON payload, triggering an autonomous workflow instantly.
Audit your team structure before deployment. Run list_agents and get_agent to see every agent's rules and backstories, ensuring the right role is assigned the job.
Control failure points directly. If a run goes wild or hits a loop, you can use cancel_run to hard-stop the process instantly without restarting the whole system.
Understand your entire capability set by calling list_crews and list_tasks. This shows you all available workflows and modular operations ready for immediate use.
See it in action
Market Research Deep Dive
A Product Manager needs to synthesize competitor data. They call kickoff_crew with a JSON input: {'topic': 'Competitor X pricing changes'}. The system runs the research, and they receive a consolidated report in minutes.
Debugging Failed Workflows
A Developer finds a run failing. They use get_run_status to check the explicit cloud logs and immediately pinpoint whether the failure is due to an API rate limit or bad input data.
Verifying Agent Roles
An AI Architect needs proof that their 'Legal Reviewer' agent has specific guardrails. They use get_agent to retrieve the structured rules and billing constraints, confirming compliance before launch.
Building a Task Catalog
A DevOps team member wants to see all possible integration points. They run list_tasks to get an automated list of every available modular operation, ensuring no critical step is forgotten.
The honest tradeoffs
Manual Status Polling
Calling a tool repeatedly in rapid succession just to see if the process is still running. This wastes tokens and provides limited context.
Instead, use get_run_status once; it gives you comprehensive cloud logging that explains why the run is stalled or progressing.
Ignoring Dependencies
Assuming Agent B can start its work without knowing what data Agent A actually produced. The process breaks at a handoff point.
First, use get_crew to extract the full property map of the crew; this shows exactly where Agent A’s output feeds into Agent B's input.
Starting without Scope
Simply triggering a workflow with no defined scope, leading to agents wandering aimlessly and producing unreviewable results.
kickoff_crew requires structured JSON inputs. Always define the initial problem in that payload so the whole crew knows its mission.
When It Fits, When It Doesn't
Use this MCP if your goal is managing stateful, asynchronous processes where multiple independent AI roles must collaborate to achieve a single outcome. You need visibility into how the process fails or stalls—that's what get_run_status and cancel_run provide.
Don't use this if you just need simple data retrieval (e.g., getting a list of users). For that, a single-function tool is better. If your workflow is highly linear and never changes, consider building it into a custom script rather than relying on the full multi-agent orchestration layer.
Questions you might have
How do I start a new workflow using kickoff_crew? +
You initiate a run by calling kickoff_crew and providing a structured JSON payload that defines the goal. The agent then takes over, executing the entire multi-agent sequence automatically.
What is get_run_status for? +
get_run_status pulls detailed cloud logging about an ongoing run. It’s how you figure out if a process failed because of resource limits or bad data, instead of just getting a vague 'Failed' message.
Can I stop a running agent team using cancel_run? +
Yes. cancel_run sends an instant signal to hard-stop any active workflow. This is useful if the agents get stuck in a loop or start going off track.
How do I check all available agent roles? Use list_agents. +
list_agents enumerates every role that can be part of your crew. This gives you a complete inventory and allows you to verify the rich details for each individual agent.
What information does using get_crew provide about a crew's structure? +
It performs structural extraction of properties that drive the account logic. You get a complete map of all components, letting you verify exactly what data dictates the workflow before kicking off an autonomous run.
How do I track asynchronous results after using list_webhooks? +
It identifies the exact validation criteria for async outcomes. This lets you monitor where a crew's final results go, even if they exit standard JSON boundaries and requires manual oversight.
Before starting a run, what does list_tasks check? +
It runs an automated validation check that routes the explicit gateway history. You confirm all required tasks are properly routed and ready to execute before committing resources or triggering a crew.
What specific rules can I see using get_agent? +
It enumerates the explicitly attached structured rules defining active billing constraints for that agent. You verify the precise operational guidelines governing its behavior and scope.
Can my agent kickoff a new CrewAI workflow? +
Yes. Use the 'kickoff_crew' tool. Provide the Crew ID and a JSON object with the required inputs. The agent will activate the multi-agent processing immediately, returning a run ID for tracking.
How do I monitor the progress of an active agent run? +
Use the 'get_run_status' tool with your Crew ID and Run ID. Your agent will grab the live execution state, showing you which agents are currently working and which tasks have been completed.
Can I cancel a running crew via the agent? +
Absolutely. The 'cancel_run' tool dispatches an instant interrupt signal to the CrewAI platform, hard-stopping active LLM contexts and terminating the execution flow immediately.
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