Polaria MCP for AI. Manage contacts, conversations, and FAQs in one flow.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Polaria MCP Server connects your AI agent directly to a unified customer support platform. It lets you manage user contacts, pull up full chat logs, track ticket statuses, and retrieve instant FAQ answers—all from one conversation flow.
Instead of switching between Polaria, your CRM, and your knowledge base, your agent does it all automatically.
What your AI can do
Create contact
Adds a brand-new customer record to your Polaria contact database.
Add chat message
Sends a message into an existing Polaria conversation thread.
Get contact
Retrieves the full profile and details for one specific user or contact.
Retrieves specific customer information by running get_contact.
Adds a message to an ongoing chat thread using the add_chat_message tool.
Returns a list of all available customer records via list_contacts.
Gathers details for an entire chat thread using the get_conversation tool, letting you see the full context.
Pulls a list of available FAQ articles with list_faqs, allowing your agent to find answers immediately.
Adds new customer records into Polaria using the create_contact tool.
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Polaria MCP Server: 8 Tools for Support Ops
These eight tools let your AI agent perform every action needed in a modern support desk—from checking user details to updating conversation status.
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 Polaria on VinkiusCreate Contact
Adds a brand-new customer record to your Polaria contact database.
Add Chat Message
Sends a message into an existing Polaria conversation thread.
Get Contact
Retrieves the full profile and details for one specific user or contact.
Get Conversation
Pulls all historical messages and status updates from a single chat conversation.
List Contacts
Generates a list of multiple contacts, letting you see who's in the system.
List Conversations
Retrieves a summary of recent chat threads and their current status.
List Faqs
Lists all available FAQ articles in Polaria, helping your agent find the right answer quickly.
List Widgets
Displays a list of customizable widgets available within the Polaria platform.
Security and governance baked right in.
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Choose How to Get Started
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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
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Make Your AI Do More
Start with Polaria, 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
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
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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 8 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Managing support history shouldn't feel like switching between five different tabs.
Today, an agent handling a request has to copy the user ID from the chat window into the CRM. Then they open the ticket management system to check status. Finally, they switch over to the knowledge base to find the answer. It’s slow, it's painful, and data gets lost in the handoffs.
With Polaria MCP Server, your AI client manages all this. The agent runs `get_contact` for user details, checks ticket status via `list_conversations`, and pulls help articles using `list_faqs`. You get a single, real-time answer without ever leaving the chat interface.
Polaria MCP Server: Make sure every customer interaction is recorded.
Manually logging notes or updating ticket status requires multiple clicks and remembering to switch between systems. You risk human error, missing key details about the user's journey before they even call back.
The agent handles this automatically. After responding, it can run `add_chat_message` and update the record immediately. The entire interaction—the question, your reply, and the status change—is recorded in one atomic step.
What your AI can actually do with this
This server plugs your AI agent directly into Polaria's entire customer support setup. Instead of making you jump between your chat window, your CRM, and your knowledge base, it lets your agent handle everything automatically. It’s all about keeping the conversation flowing without dropping context.
Managing People Records
Your agent handles every angle of the user profile. You can use list_contacts to pull up a comprehensive roster of everyone in the system, letting you see who's signed up. If you need details on one specific person, running get_contact pulls their full profile and all the background data. Don't have a record for a new customer? No sweat; your agent can use create_contact to add that brand-new user right into Polaria’s database.
Tracking Conversations and History
When someone chats with you, this server gives your agent total visibility. You'll use list_conversations to grab a quick summary of recent threads and check their current status at a glance. If the user needs you to dive deep into what was said, get_conversation pulls every single message and status update from that whole chat history so nothing gets missed.
When it’s time for your agent to reply, running add_chat_message sends a response right into the existing Polaria thread. You're always replying in context.
Finding Answers and System Details
Your agent doesn't have to guess what answer to give. It can use list_faqs to list every available article in Polaria, giving your agent the data it needs to find a correct answer fast. You can also check out system capabilities by running list_widgets, which displays all the customizable widgets that are set up within the Polaria platform.
This setup lets your AI client do three things: Get and Create Contacts using get_contact, create_contact, and list_contacts; Control Chat Flow with tools like get_conversation and add_chat_message; and Access Knowledge by listing available articles via list_faqs. It’s a full-stack connection that keeps your agent working right where you need it to.
019dd13e-fb1c-730d-8996-16fc389aba89 Here's how it actually works
The bottom line is: your AI agent talks to Polaria's API through Vinkius, letting it perform real actions like updating status or fetching contact details.
Log in to your Polaria dashboard and create a new application under Settings > Marketplace. This gives you the necessary Secret Key.
Connect your AI client using Vinkius, passing it the Polaria credentials. Your agent now has access to all defined tools.
Your AI client calls the tool (e.g., get_contact). The server executes the action against Polaria and sends back a structured JSON payload with the data.
Who is this actually for?
This is for customer success teams and support operations leads who hate the constant context switching. If your agents spend more time navigating dashboards than talking to customers, this is for you. You need one single place where data retrieval, messaging, and ticket status updates all happen automatically.
You use get_contact and list_faqs to immediately pull up a user's history and the relevant help article while you are chatting with them.
You manage campaign outreach by using create_contact or bulk review lists via list_contacts, keeping all customer data centralized for targeted follow-ups.
You build agent workflows that automatically update ticket status (Open, Resolved) and record chat messages (add_chat_message) after the conversation ends.
What Changes When You Connect
Full Context on Demand: Instead of reading through multiple tabs, your agent runs get_conversation to pull up the entire chat history instantly. This keeps you focused on solving the problem, not finding the data.
Centralized Data: Use list_contacts and get_contact to manage all customer records inside the agent workflow. You never have to leave the conversation view to check a user's status or details.
Instant Knowledge Lookup: The ability to call list_faqs means your agent pulls verified help articles directly into the chat, resolving inquiries without needing a human handover to the knowledge base team.
Automated Status Updates: Your workflow can automatically update ticket statuses and log messages using tools like add_chat_message, making sure nothing falls through the cracks after an interaction.
On-the-Fly User Creation: Need to talk to a new client? Use create_contact within your agent's logic. It captures all necessary details immediately, ensuring no lead is missed.
See it in action
Client calls with vague issues
A user chats in saying 'my account isn't working.' The agent doesn't know where to start. It first runs list_conversations to see the last chat topic, then uses get_contact to pull up their account details and history. Finally, it runs list_faqs to find a guide on 'Account Access Issues', delivering an immediate solution.
Handling new leads
A lead fills out a form via chat. The agent uses get_contact first to check if the person exists, and if not, it calls create_contact. It then sends a personalized welcome message using add_chat_message, turning a raw inquiry into an organized record.
Reviewing complex tickets
An agent needs to understand why John Doe called three weeks ago. They use get_conversation on the ticket ID, reviewing all past messages and status changes. This gives them full context before they reply, preventing repeated troubleshooting steps.
Bulk customer outreach
The CS team needs to follow up with 50 users who had 'Billing Issue' tickets. They call list_contacts to filter by the required attributes and then use those details to trigger specific, personalized messages via the agent.
The honest tradeoffs
Calling APIs manually
A developer writes separate client-side code blocks for list_contacts, then another block for get_contact using the ID. This makes the integration brittle and complex.
Instead of writing multiple functions, let your agent decide which tool to use. If it needs a single contact's data, it calls get_contact. If it needs a list, it calls list_contacts. The AI decides the flow, not the developer's code structure.
Missing conversation context
An agent responds to a chat message without first running get_conversation, leading to an answer that misses critical background details or prior steps in the user's journey.
Always start by calling get_conversation when responding. This gives your agent all the necessary history, ensuring every reply is informed and accurate.
Assuming data existence
The workflow tries to update a ticket status for an ID that was never created or has been deleted. The process fails with a generic error.
Before acting, check the source of truth. Run get_contact first to validate the user exists, and run list_conversations if you need to verify a recent chat thread before attempting to modify it.
When It Fits, When It Doesn't
Use this server if your core problem is unifying support data: contact records, chat logs, and knowledge articles. If your agent needs to know who the user is (get_contact), what they talked about (get_conversation), or how to answer questions using official docs (list_faqs), you need Polaria. Don't use it if your goal is purely internal ticketing—if all you do is change a status without interacting with contacts or conversations, other simple backend tools might suffice. The key value here is the ability to perform data retrieval AND state changes (like add_chat_message) in one continuous flow.
Questions you might have
How do I check a user's history with Polaria MCP Server? +
You use get_conversation. This tool pulls all messages, statuses, and context from a specific chat thread. It gives your agent the full background needed to respond accurately.
Can I add new contacts using the Polaria MCP Server? +
Yep, you use create_contact. This tool takes necessary data fields and adds a brand-new record directly into your main customer database within Polaria.
What is the difference between `list_contacts` and `get_contact`? +
list_contacts gives you a roster—it shows multiple contacts. Use it when you need to review several users. get_contact drills down, giving you all the specific data for one single user.
Does Polaria MCP Server handle ticket status updates? +
Yes, by managing conversations and contacts, your agent can update statuses internally. You manage this capability via structured actions after gathering data with list_conversations.
Are there rate limits when using the `list_conversations` tool? +
Yes, Polaria enforces API call rates to maintain stability. We recommend batching your requests and implementing exponential backoff if you hit a limit. Typically, the system allows 100 calls per minute for standard usage.
What should I do if `add_chat_message` fails? +
Check the error code returned by the tool first; this tells you exactly what went wrong. Common issues include an invalid conversation ID or insufficient permissions on your connected account. Double-check all required parameters.
How can I filter results when using `list_faqs`? +
You must pass specific filters to the list_faqs function, such as a keyword or category ID. The tool returns structured data that allows you to narrow down results by content type or date last updated.
What parameters are required when I call `get_contact`? +
The get_contact tool absolutely requires a unique contact ID (CID) and optionally an API key for authentication. Providing the CID ensures the agent retrieves one specific record instead of listing all users.
Where do I find my Secret Key? +
Log in to Polaria, go to Settings > Marketplace > Create my own app, create a new application, and show the 'Secret Key' in the authorized applications section.
What is the Base URL for the API? +
The Polaria REST API v2 base URL is: https://polaria.ai/rest/v2/
Can I modify existing conversations? +
Yes, you can add replies and change the status of existing conversations directly using the Polaria MCP.
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