UnifyApps MCP for AI. Audit all workflows and integration health from chat.
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UnifyApps lets your AI agent take over complex system monitoring. You can query all integrations, list every defined automation flow, check active connections, and audit execution logs—all in a single chat interface.
It’s built for debugging massive SaaS orchestration systems.
What your AI can do
Get integration details
Retrieves specific information and status for one integration by its ID.
List ai agents
Retrieves a list of every AI agent configured in the UnifyApps system environment.
List active connections
Lists all active accounts that are currently connected to UnifyApps.
Retrieves a full inventory of applications coupled in UnifyApps using the list_integrations tool.
Queries recent run logs, showing success/failure metrics for specific automation flows via list_flow_executions.
Retrieves a structured view of every possible automation flow blueprint using list_automation_flows.
Verifies the status and details of any specific integration connection using get_integration_details or list_active_connections.
Lists all AI agent systems that are currently configured within the UnifyApps environment via list_ai_agents.
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UnifyApps MCP Server: 6 Tools for Integration Management
Use these tools to query connectivity status, list all flows, and track historical run data across your entire automation platform.
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 UnifyApps on VinkiusGet Integration Details
Retrieves specific information and status for one integration by its ID.
List Ai Agents
Retrieves a list of every AI agent configured in the UnifyApps system environment.
List Active Connections
Lists all active accounts that are currently connected to UnifyApps.
List Flow Executions
Retrieves a history of recent runs, showing success and failure status for specific...
List Automation Flows
Lists all defined automation workflow blueprints within the platform.
List Integrations
Lists every configured application that is integrated into UnifyApps.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
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 UnifyApps 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 UnifyApps, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ 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 UnifyApps. 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 6 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Debugging automation failures shouldn't require clicking through five different dashboards.
Today, a silent failure means jumping between the 'Logs' tab to see when it stopped, then switching to 'Integrations' to check if the key is expired, and finally hitting 'Flows' to confirm the workflow blueprint hasn't been accidentally deleted. It’s manual, slow, and you always miss something.
With UnifyApps MCP Server, you just ask your agent: 'Why did the payment fail?' The agent runs `list_flow_executions` and immediately reports back: 'The flow ran successfully but failed because the Stripe connection ID is expired.' You get a direct answer without leaving the chat.
UnifyApps MCP Server: Get a full audit of all connections.
Manual audits require dumping reports from dozens of separate services, manually comparing ID numbers across platforms to ensure no service is running without proper credentials. It's tedious and prone to human error.
Now, you run `list_active_connections` or `list_integrations`. The agent pulls the full list in a structured output instantly. You verify all linkages against a master checklist—no copy-pasting required.
What your AI can actually do with this
UnifyApps lets you take over complex system monitoring. You can query every integration, list every defined workflow blueprint, check active connection credentials, and audit execution logs—all from a single chat interface. This thing is built for debugging massive SaaS orchestration systems.
Inventorying What's Coupled
You need to know what services are hooked up before you can fix anything. You'll get a full inventory of every application coupled into UnifyApps using list_integrations. That gives you the complete picture of your connected components. If you gotta check on credentials, you've got two ways: first, run list_active_connections to see all accounts that are currently logged in and running; or, if you know exactly which connection is giving you trouble, use get_integration_details(ID) to pull up specific status information for just that one integration.
Mapping the System Logic
Figuring out how everything talks to each other can be a nightmare. You'll get a structured view of every single possible automation flow blueprint by calling list_automation_flows. This lets you map out all your workflows without clicking through nested visual menus and getting lost. For managing the brains behind this operation, use list_ai_agents to pull up a list of every AI agent system that's configured within the UnifyApps environment.
Tracking Failures and Successes
When something breaks, you need history—you don't need guesswork. You can run list_flow_executions to query recent run logs for any specific automation flow. This tool shows you a clear record of the execution status, letting you see which runs succeeded and which ones failed. That historical data is your primary debugging resource.
How It Works in Practice
Think of this server like a deep-dive console for an operations team. You don't have to jump between five different dashboards to check the health of your whole setup. Instead, you tell your agent what you need—like 'show me all connections that failed last week,' or 'list every integration I've used.' The system talks back with specific data points pulled directly from the tools we laid out.
If you find an issue, say a flow isn't running right, you can start by calling list_automation_flows to verify the blueprint is correct. Then, check that same flow's execution history using list_flow_executions. If the logs look good but it still fails, maybe you gotta dig into the credentials. You'll use get_integration_details(ID) on that specific app to make sure the token hasn't expired or gotten revoked.
It’s a methodical process: map it, check its connections, and then audit the history.
You never have to guess what kind of system you're running. You pull a list of all agents with list_ai_agents, verifying that every component—from your most basic connected service (list_integrations) to your complex multi-step workflow—is accounted for and working as it should.
019d7619-3667-738f-8b8c-6ce99fcc284b 
Here's how it actually works
The bottom line is that you talk to your agent, and the agent runs the necessary API calls against UnifyApps to give you a single-source view of everything connected.
Manually subscribe to this server logic through your connected portal.
Provide your overall UnifyApps Base Platform URL and API key to the agent.
Direct queries (e.g., 'What failed in the last hour?') to your AI client, letting it use the available tools to pull live data.
Who is this actually for?
SREs, DevOps engineers, and Enterprise IT Managers. You're the person who wakes up at 2 AM when an automation job fails silently because a credential expired or a single endpoint changed. This server lets you track down the failure point without clicking through three different dashboards.
Uses list_flow_executions to rapidly check API failures and debug run history, saving hours of manual log diving.
Runs list_integrations and list_automation_flows to verify the structural skeleton connecting services before a major deployment.
Uses list_active_connections to audit current connection linkages across global platforms, preventing unauthorized access endpoints.
What Changes When You Connect
Saves time debugging. Instead of checking ten separate dashboards to see why a flow failed, you run list_flow_executions and get a centralized report showing exactly when the failure occurred and where.
Maintains security posture. Use list_active_connections to pull massive status lists verifying which credentials are currently linked to prevent unauthorized access endpoints from being missed.
Maps complex systems quickly. Need an overview of how everything runs? Run list_automation_flows to see the structural skeleton, avoiding manual navigation through nested menus.
Streamlines agent management. Use list_ai_agents to get a full inventory of all AI agents plugged into your system, ensuring no orphaned or forgotten services exist.
Reduces discovery overhead. With list_integrations, you immediately see every app connected (Salesforce, Stripe, etc.) without having to check the platform UI manually.
See it in action
Investigating a payment failure
A customer reports an order failed. You ask your agent to check the logs. The agent uses list_flow_executions and identifies that the last three Stripe payments timed out. You then use get_integration_details on the Stripe connection ID, confirming it's a credential mapping issue, not a payment failure.
Pre-audit for compliance
Before a major audit, you need to prove every connected service is accounted for. You run list_integrations and cross-reference the output with your compliance checklist, confirming that all 42 required endpoints are present.
Debugging orphaned workflows
The team thinks a flow isn't running because someone deleted it. You run list_automation_flows to verify the blueprint exists, and then use list_ai_agents to confirm that no agents are mistakenly pointing at an outdated or non-existent workflow ID.
Initial system onboarding
A new service needs connecting. Instead of asking a teammate for documentation, you run list_active_connections first. This shows you which connection types are already active (e.g., 'Outlook', 'Salesforce'), helping you understand the required credential format immediately.
The honest tradeoffs
Manual UI navigation
Clicking through five different tabs—Logs, Integrations, Agents, Flows—to piece together a single failure report. This takes 15 minutes and requires switching contexts constantly.
Send one prompt to your agent: 'Show me the execution history for flows linked to Salesforce.' The agent runs list_flow_executions and filters by the relevant integration, giving you the answer in seconds.
Assuming connection status
Seeing a workflow run successfully in the main dashboard, but failing later because of an expired API key. The dashboard hides this specific credential failure.
Always explicitly check credentials using get_integration_details for critical connections. Don't trust a 'green light' status without verifying the underlying connection ID.
Ignoring agent scope
Thinking that simply listing all integrations means you know which ones are actually in use by your agents.
Run list_ai_agents first. This shows you the active compute layer, and then ask it to cross-reference those agents with list_integrations to narrow down the relevant scope.
When It Fits, When It Doesn't
Use this server if your problem involves coordinating data across multiple decoupled services (SaaS platforms). If a failure requires checking if an integration exists, if it's connected, and if the flow using it ran recently, you need UnifyApps. It ties together discovery (list_integrations), governance (list_active_connections), and execution tracing (list_flow_executions).
Don't use this if your problem is simple: If you just need to know what one API endpoint returns for a single resource, use a direct API call or an existing dedicated connector. This server adds overhead because it manages the orchestration layer itself; it’s overkill for point-to-point data transfers.
Questions you might have
How do I use list_flow_executions to find a specific failure? +
You ask your agent to run list_flow_executions and filter the results by time range or flow name. The output provides success/failure status for recent runs, allowing you to pinpoint exactly when and where the process broke.
Can list_integrations tell me which apps are connected? +
Yes. list_integrations retrieves a full inventory of every application coupled in UnifyApps. This gives you a complete, single source list of all available integration types.
What is the difference between list_automation_flows and list_integrations? +
They track different things. list_integrations lists the applications (e.g., Salesforce, Stripe). list_automation_flows lists the actual blueprints or workflows built using those applications.
Does get_integration_details check if a credential is good? +
Yes, it checks the specific connection ID you provide. It reads the current status and confirms whether the credentials are authenticated and active, preventing guesswork.
Does list_active_connections show only currently running accounts, or does it report on all linked services? +
It lists every account connection configured in your hub. For each entry, you get a status flag—Active, Suspended, or Deprecated—telling you the current operational state of that link.
What metadata does list_ai_agents provide regarding compatibility and dependencies? +
The tool lists the agent's unique ID, its name, and which core SDK version it runs on. This helps you quickly verify if an integration requires a specific dependency to function.
If I suspect a connection is corrupted, how can I use list_integrations to detect the failure? +
The output of list_integrations includes a 'health status' field for each entry. If credentials are bad or connectivity fails, it reports an error code and a descriptive message right in the listing.
When I run list_automation_flows, is there a limit to how many flows it can retrieve? +
The tool supports pagination. It returns results in batches, so if your platform has more workflows than the page size you request, you'll need to call the function again using the provided next page token.
Can the agent simultaneously retrieve all recent failed/successful automation execution logs natively? +
Yes. Instigate the 'list_flow_executions' tool payload command. It bypasses internal visualization panels generating the chronological queue, so your model can deduce if major error clusters occurred instantly.
How do I easily audit what 3rd party connections are actively mounted? +
Issue a direct command targeting 'list_active_connections'. It pulls back pure lists documenting connected providers (e.g. Stripe, Salesforce) preventing hidden ghost authorizations from silently draining resources.
Does it also list internal AI Agents configured inside the platform natively? +
Yes. Utilize 'list_ai_agents' via prompt and your model retrieves every bot profile attached along with baseline configurations proving visibility isn't simply restricted to flows.
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