Birdeye MCP. Manage CX, Reviews, & Contacts in Conversation
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Birdeye connects your AI agent directly to your reputation management data. List customer reviews, pull survey responses, manage contacts, and trigger follow-up surveys—all without switching screens.
It turns complex reputation tracking into a simple conversation.
What your AI agents can do
Checkin customer
Sends an automated request for a review or survey when you check in a customer.
Get business info
Retrieves general, core information about your business account.
Get contact
Fetches specific profile and contact details for a single customer.
Get quick reports on review counts and detailed lists of customer comments from all sources.
Send automated follow-up requests for reviews or surveys when a customer checks in.
Reply to specific customer reviews right from your agent, maintaining high engagement rates.
Retrieve full profiles for contacts or list every business location managed in your account.
List available surveys and fetch specific customer responses to gauge overall satisfaction.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Birdeye: 10 Tools for CX Management
Use these tools to list reviews, track survey responses, manage customer profiles, and automate follow-ups directly from any AI client.
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 Birdeye on Vinkius019d755dcheckin customer
Sends an automated request for a review or survey when you check in a customer.
019d755dget business info
Retrieves general, core information about your business account.
019d755dget contact
Fetches specific profile and contact details for a single customer.
019d755dget review summary
Provides an overview of review counts broken down by the platform they came from.
019d755dget survey responses
Gets all recorded responses for a particular survey ID you specify.
019d755dlist contacts
Generates a complete list of customer profiles saved in your system.
019d755dlist locations
Lists all physical business locations managed under your Birdeye account.
019d755dlist reviews
Retrieves a list of raw customer reviews, showing details for each one.
019d755dlist surveys
Pulls a full listing of all the surveys you've set up in Birdeye.
019d755dreply to review
Allows your agent to draft and post a direct response to a customer's published review.
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 Birdeye, then connect any of our 4,800+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,800+ 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 Birdeye. 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 server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
The old way required switching between at least four different browser tabs just to manage feedback.
Today, handling a customer review means logging into the Birdeye dashboard. You find the review, copy the details, open your CRM to check their contact info, and then switch over again to trigger a follow-up survey request. It’s a painful click-and-copy cycle that kills momentum.
With this MCP, you talk to your agent. You ask it about the customer's feedback, and it handles all the lookups—retrieving contact details, checking review summaries, and getting location data—then provides everything right there in your chat window.
Handling Reviews with Birdeye MCP
The biggest manual step that goes away is the 'reply then switch' process. You don't have to copy a complaint, paste it into an email draft, and then go back to the dashboard to post the reply.
Now you just tell your agent: 'Draft a response to this review.' It handles pulling the context, drafting the message, and posting it directly through the `reply_to_review` tool. That's how fast.
What you can do with this MCP connector
Managing online feedback used to mean juggling dashboards: checking Google Reviews here, pulling contact lists there, and starting a new survey request in a third tool. Now you can handle your entire customer experience workflow through natural conversation.
Your agent connects directly to Birdeye, letting you ask it questions about your business reputation or run reports on specific customers. You can get a quick summary of how many reviews you've received from different sites, pull detailed records for any contact, and even automatically send out review requests when a customer checks in.
It’s all built into the Vinkius catalog, giving your AI client one centralized way to handle everything from initial data gathering to drafting responses. You can list recent reviews, check survey outcomes, or just pull up location details for a specific branch—all within your existing workflow.
019d755d-6a84-7118-a9d5-b3dd40f77548 
How Birdeye MCP Works
- 1 Subscribe to this MCP and enter your Birdeye API Key and Business ID.
- 2 Your AI client accesses the connected tools, allowing you to request specific data like a customer's profile or recent reviews.
- 3 The agent executes the tool function and returns structured data directly into your conversation thread for immediate action.
The bottom line is, it lets your AI agent run complex reputation workflows without needing you to navigate multiple business portals.
Who Is Birdeye MCP For?
This MCP is built for anyone who lives in the gap between sales and customer service. It’s needed by folks whose job requires constant cross-referencing: checking a review, finding the contact details, and then sending an automated follow-up survey.
Checks recent reviews to see who needs a reply and uses the tool to respond directly, all in one place.
Monitors survey results or checks contact details to follow up with customers who haven't responded yet.
Retrieves review summaries and location data to build reports for quarterly business reviews.
What Changes When You Connect
- Stop switching tabs. You can use the agent to pull a list of reviews and immediately reply using the
reply_to_reviewtool—all without leaving your chat window. - Automate follow-up requests. Use
checkin_customerwhen you process a client, and Birdeye handles sending out the review or survey request automatically. - Get high-level data fast. Instead of reading 50 reviews to guess sentiment, ask for the summary using
get_review_summaryand know exactly what's going on across platforms. - Centralize customer data. You can list all contacts with
list_contacts, then drill down into a specific person's details usingget_contactbefore starting an outreach campaign. - Track satisfaction systematically. Use
list_surveysto see what you have, then pull the actual results for any survey viaget_survey_responsesto pinpoint feedback trends.
Real-World Use Cases
A customer complains about a service gap.
The agent pulls up the contact details using get_contact, checks the location history with list_locations, and drafts an apology reply to the review using reply_to_review. This keeps the conversation focused on solving the problem.
Need a quick report for leadership.
The agent first runs get_review_summary to get total counts by source. Then it calls list_reviews to pull 10 recent examples and summarizes them into a single, clean status update.
Onboarding new local partners.
The agent uses get_business_info for general setup data, then runs list_surveys to ensure the partner has access to all necessary feedback forms. It confirms everything is ready.
Processing a lead appointment.
Instead of sending a generic email, the agent first uses checkin_customer on the new lead's contact details, which triggers an automated survey request right away.
The Tradeoffs
Treating it like a database query
Asking the agent for 'all data about reviews and contacts.' This is too vague and forces the AI to guess which tools you mean, leading to poor or incomplete results.
→
Be specific. Instead of general requests, ask: 'What were the last three reviews from Google?' (using list_reviews) or 'Show me John Doe's contact details.' (using get_contact). Always specify the desired output.
Missing context between tools
Asking to reply to a review without first running list_reviews and providing the specific link or ID. The agent doesn't know which review you mean.
→
Always establish context first. Use list_reviews to show all options, then follow up by saying: 'Using that second one listed, please reply to it.' (using reply_to_review).
Assuming data is always fresh
Relying on stale information. If you forget to run list_locations, your agent might use old or incorrect address details.
→
Verify critical setup data first. Always call list_locations when planning a multi-site campaign, so the agent works with current operational addresses.
When It Fits, When It Doesn't
Use this MCP if your job requires connecting customer feedback to internal action. Think of it as the single source of truth for reputation data: you need to list reviews and get contact details and trigger a follow-up survey.
Don't use this if your main task is just writing blog posts or generating general content—that’s a language model job. If you only need raw, unstructured text, using list_reviews might be enough. But if you need to act on that data (like replying, summarizing, or sending requests), then the full suite of tools like get_review_summary, reply_to_review, and checkin_customer is exactly what you need.
Common Questions About Birdeye MCP
How do I use Birdeye MCP to get a summary of my reviews? +
You call the get_review_summary tool. It doesn't give you every review; it gives you a high-level count, showing how many reviews came from Google versus Facebook or Yelp.
Can I use Birdeye MCP to automatically send survey requests? +
Yes, by using checkin_customer. When your agent checks in the customer's contact details, it triggers the automated process to request a review or survey.
What is the difference between list_reviews and get_review_summary? +
list_reviews gives you the raw data—you see all the text and stars. get_review_summary only gives you aggregate numbers, like total 5-stars or total reviews by source.
Do I need to know my customer's ID for get_contact? +
Yes, the agent needs enough information (like an email or name) to pinpoint the correct contact profile. It can use get_contact to pull their full details once that identification is made.
What happens if I use get_contact and the contact record doesn't exist? +
The API returns a specific error status code and an explicit message like 'Contact Not Found.' Your agent reads this feedback to know exactly why the request failed. You can then adjust your workflow, either skipping that contact or correcting the input data.
Does list_reviews handle thousands of customer reviews? Is there a limit? +
No, it handles large volumes because it supports pagination. The MCP doesn't send all records at once; you request subsequent pages using provided cursor or page number parameters. This keeps your agent calls efficient and prevents hitting data limits.
How do I build a workflow to reply to multiple reviews using list_reviews and reply_to_review? +
You first use list_reviews to pull the necessary review IDs. Your agent then iterates through those results, calling reply_to_review for each specific ID you want to respond to. This sequence lets you process large batches of feedback in a controlled way.
How do I link general reviews or contacts to a single store location using list_locations? +
You must call list_locations first to retrieve the correct business ID. When you use other tools, like fetching review summaries, you pass that specific Location ID as a mandatory parameter. This ensures all data is correctly scoped and tied to one physical site.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.