Typeform MCP. Extract raw responses or inspect form architecture.
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Typeform MCP Server connects your AI client directly to Typeform data. You can fetch raw submissions, inspect form layouts for structural details, or pull completion insights without needing the heavy web panel.
This lets you process survey responses and manage forms entirely within your chat context.
What your AI agents can do
Get form details
Retrieves the full structure and metadata for any single Typeform instance.
Get form insights
Pulls aggregated analytics, including completion rates and average scores, for a form ID.
Get form responses
Retrieves all submitted answers for a specified Typeform ID into memory.
Shows you every workspace associated with your Typeform account.
Lists all the form IDs currently set up in your account.
Retrieves the blueprint of a specific form, letting you see its questions and components.
Gathers all user responses for a given form ID into usable data arrays.
Pulls analytical data, like average scores and completion rates, for specific forms.
Retrieves a list of available design themes you can use on your forms.
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Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Typeform MCP Server: 6 Tools for Form Operations
Use these six tools to manage everything from listing available workspaces and finding forms to extracting raw responses and analyzing completion metrics.
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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 Typeform on Vinkius019d7617get form details
Retrieves the full structure and metadata for any single Typeform instance.
019d7617get form insights
Pulls aggregated analytics, including completion rates and average scores, for a form ID.
019d7617get form responses
Retrieves all submitted answers for a specified Typeform ID into memory.
019d7617list form themes
Shows you a list of visual design themes available to apply to your forms.
019d7617list forms
Provides a simple list of all active form IDs within the Typeform account.
019d7617list workspaces
Lists every distinct workspace ID available in your connected Typeform account.
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Start with Typeform, then connect any of our 4,800+ other servers whenever your AI needs more. One click, no limits.
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Typeform. 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 6 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Dumping survey answers usually means clicking five different times.
Today, getting a full picture is a pain. You have to log into Typeform, navigate to the correct workspace, find the form ID, then download the submissions as CSVs. If you need sentiment analysis, you dump those files into another service, clean them up, and finally feed them back to your LLM. It's slow, messy, and involves a ton of copy-pasting.
With this MCP server, that whole dance disappears. You just tell the agent which form ID you want, and it runs `get_form_responses`. The raw data lands right in your chat context—clean, structured, and ready for analysis instantly. No files, no exports, just pure input.
The Typeform MCP Server: Get the full blueprint with `get_form_details`.
Before you can write a single line of code or run an analysis, you need to know how the form is built. Manually inspecting a complex survey means clicking through dozens of conditional branches and question types just to map out the logic for someone else. It’s tedious work done inside a browser's visual editor.
Now, use `get_form_details`. The server pulls the entire architectural blueprint—the questions, the field IDs, the branching rules—and gives it back as clean metadata. You get the full map without ever leaving your terminal or chat interface.
What you can do with this MCP connector
Finding Your Way Around Typeform Data
Your AI client hooks up straight to your Typeform account, bypassing the bulky web panels you usually gotta mess with. You don't need to click through dashboards or download CSVs just to get answers; this server lets your agent pull everything into memory instantly. It's all about getting the raw data and structure right inside your chat context.
Mapping Your Environment
You start by figuring out what you’re working with. You can use list_workspaces to see every distinct workspace ID connected to your account—that gives you a clean map of where everything lives. From there, if you need to know which forms are active, just run list_forms. This spits out a simple list of all the form IDs currently set up in Typeform.
Inspecting Form Structure and Design
Once you have an ID, you gotta check what that form looks like structurally. The get_form_details tool pulls the blueprint for any specific Typeform instance; it lets you see the full metadata and question components without ever having to open the visual editor. You can inspect the underlying flow logic and all the questions used.
Need a design refresh? list_form_themes retrieves a list of every available visual theme you can apply, so you know what looks good before you commit.
Pulling Raw Responses and Insights
When it comes to data, this thing is fast. If you need all the user submissions for a given form ID, get_form_responses gathers every single submitted answer into usable data arrays right in your client's memory. This is way cleaner than dealing with manual file dumps cluttering up your folders.
Beyond raw answers, if you want to analyze what people actually thought, use get_form_insights. This tool pulls the aggregated analytics for a specific form ID, giving you key numbers like average scores or completion rates without forcing you to wade through complex reporting dashboards. It delivers the metrics straight up.
019d7617-13f7-7301-a84c-e7433b4944be 
How Typeform MCP Works
- 1 First, secure an official connection token directly within the Typeform platform and lock it to this bridge implementation.
- 2 Next, tell your agent what you need—query for a form ID using
list_formsor specify the raw data you want fromget_form_responses. - 3 Your AI client executes the command, pulling the targeted metadata, responses, or insights directly into the chat context.
The bottom line is: You treat your Typeform account like a database accessible purely through conversation commands.
Who Is Typeform MCP For?
This server is for the CX planner who gets frustrated trying to reconcile raw survey data with clean analytics. It's also for designers who need to map out complex form flow logic without actually opening the editor in a browser.
Uses get_form_insights to quickly compare performance data across multiple forms and identify which survey areas are failing.
Runs get_form_responses on specific IDs, pulling raw feedback to manually analyze sentiment or flag key accounts for follow-up.
Calls get_form_details to inspect a form's logic and branching structure without having to navigate the visual editor.
What Changes When You Connect
- You get immediate sentiment analysis using
get_form_insights. Instead of manually calculating averages from exported data, the agent pulls metrics directly and passes them to your LLM for instant summary. - Bypass CSV dumps entirely. Use
get_form_responsesto pull raw submissions programmatically. This keeps sensitive feedback secure in your chat context instead of cluttering file storage. - Inspect form logic without opening a browser. The
get_form_detailstool lets you map out flow branching and question types, which is critical for designers who need structural blueprints. - Quickly locate the right survey. Use
list_workspacesfollowed bylist_formsto filter through dozens of campaigns and pinpoint the exact form ID needed for analysis. - View design options with
list_form_themes. You can check which visual themes are available before committing time to styling, saving you from unnecessary UI exploration.
Real-World Use Cases
Analyzing a large job satisfaction survey
The HR analyst needs to know the overall mood. They ask their agent to run get_form_insights on the main ID. The agent pulls completion rates and average scores, allowing the user to immediately summarize general sentiment without ever touching an export.
Debugging a complex onboarding flow
A UX designer suspects a form branch is broken. They use get_form_details to get the structural blueprint. This shows them the intended logic, allowing them to pinpoint exactly which question type or field ID needs correction.
Consolidating feedback from multiple campaigns
A Product Manager has dozens of surveys spread across different client groups. They first use list_workspaces and then iterate through forms using list_forms. Finally, they run get_form_responses on each one to pass all raw data into a single AI prompt for comprehensive analysis.
Validating form setup before launch
A developer needs to ensure the survey is set up correctly. They use list_forms to get the ID, then run get_form_details. This confirms all question types and metadata are present before any real users submit data.
The Tradeoffs
Trying to dump everything in one go
Asking the agent, 'Give me every piece of data about all our forms.' This is too vague and forces the system into a generic error or an overly massive, unmanageable output.
→
First, use list_workspaces to narrow the scope. Then, run list_forms on that workspace. Finally, select one specific ID and request data using get_form_responses.
Ignoring structure for raw data
Simply running get_form_responses without first checking the form’s design. This provides thousands of answers but gives no context on why those answers were submitted or if the fields are correctly mapped.
→
Always check the architecture first. Run get_form_details to understand the field map and question hierarchy before pulling data with get_form_responses.
Manually comparing analytics
Exporting a CSV, jumping into Excel, calculating averages, and then trying to cross-reference that against screenshots of completion rates.
→
Let the agent handle it. Use get_form_insights to pull already aggregated metrics directly into your context. The data is ready for immediate analysis.
When It Fits, When It Doesn't
Use this server if your core problem involves querying, analyzing, or structuring data from Typeform surveys. You need the ability to fetch raw answers (get_form_responses) or view the metadata/structure (get_form_details). Don't use it if you just need a simple list of forms; list_forms handles that. Also, don't rely on this for actual form creation or editing—it only reads data and structure. If your goal is to compare performance metrics across multiple sources (e.g., Typeform vs. Google Forms), you should use an integrated data warehousing tool instead of just relying on the get_form_insights tool alone.
Common Questions About Typeform MCP
How do I find all my active Typeform surveys using list_forms? +
Use list_forms to pull a plain list of every form ID in the account. This is faster than navigating the web panel and gives you an immediate inventory of what's available for analysis.
What is the difference between get_form_responses and get_form_insights? +
The difference is raw data versus summary. get_form_responses pulls every single answer submitted (the messy, detailed input). get_form_insights gives you clean metrics like average scores or completion percentages.
Can I see what themes are available for my forms using list_form_themes? +
Yes, running list_form_themes retrieves all the visual design options attached to your Typeform account. This helps you decide on a look before applying any changes.
Do I need an API key for get_form_responses? +
You must provide a verified connection token in the setup process, which authenticates the agent to your Typeform account. The server handles the secure use of that token when you invoke get_form_responses.
Before listing forms, how do I use `list_workspaces` to determine which environment my target form belongs to? +
It lists all available workspaces associated with your account token. You need this list first because Typeform often runs multiple instances across different scopes; knowing the workspace ID lets you scope your subsequent commands correctly.
When I use `get_form_details`, how does it handle conditional logic or complex question types? +
It pulls the full metadata blueprint, including conditional jumps and field types. You get a structured array showing which questions are visible based on user input, letting you map the flow outside of Typeform.
If I run `get_form_responses` with an invalid Form ID, what specific error message should I expect? +
The system returns a clear 'Form not found' status code and specifies the required format for the input. This lets your AI client catch bad IDs immediately in the script without crashing the whole workflow.
What metrics does `get_form_insights` provide that aren't available through raw data from submissions? +
It delivers calculated analytics like completion rates, average time spent, and drop-off points per segment. This gives you high-level performance summaries right out of the box for quick analysis.
Can I publish a new dynamic Typeform directly from a conversational command? +
No, this architecture specifically focuses purely and strictly on ingesting incoming feedback payloads and analyzing structures. Complex visual form generation natively lacks API flexibility conducive to safe autonomous LLM builders efficiently avoiding poor user interfaces natively constructed blindly.
Are hidden variables pulled alongside standard answers when retrieving responses? +
Yes! Extracted payloads contain an aggregated matrix detailing exact answers seamlessly mixed with tracking parameters natively gathered enabling profound correlation matching intelligently done effortlessly inside the AI model natively securely attached perfectly.
Does pagination protect aggressive limits natively for massive sets of answers? +
Totally protected. Vurb's native egress filtering dynamically manages page streams safely avoiding out-of-context faults gracefully when looping through immense objects autonomously parsing inputs effortlessly natively correctly securely stably unified absolutely correctly.
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