Fairing
Fairing provides post-purchase attribution surveys, question management, and response data for measuring marketing performance.
Verdict
Common use cases
- Analyze attribution survey responses by cohort
- Spot trends in NPS feedback over time
- Tag open-ended answers for product themes
- Compare pre- and post-campaign response rates
- Surface top customer complaints from surveys
Integration
- Vendor
- Fairing
- Category
- other
- Auth
- API_KEY
- Tools
- 2
- Composio slug
fairing
Tools
- List Questions
Return the connected shop's complete Fairing question stream, optionally including targeting rules.
- List Responses
Return one page of the connected shop's survey responses, with optional question, time-window, and sort filters.
Setup
Setup guide
- 11. In Switchy, open Settings and navigate to Integrations. 2. Find Fairing in the list and click Connect. 3. You'll be prompted to paste an API key — retrieve this from your Fairing dashboard under Settings > API Access. 4. Paste the key and click Authorize; Switchy validates the connection immediately. 5. Return to any Project and type '@Fairing list the questions in our survey stream' to confirm the integration works. 6. If you see question IDs and text, you're ready; if you get an error, double-check the key has read permissions in Fairing.
What teammates see: by default, memories from Fairing are scoped to the Project (PROJECT visibility) - you can mark any memory PRIVATE or share it ORG-wide.
Works well with
Top models
Compatibility data appears once enough Projects have used this MCP together with a given model.
How Switchy teams use it
Starter prompts
List All Survey Questions
@Fairing list all questions in our survey stream, including any targeting rules that are active.
Recent Attribution Responses
@Fairing return the most recent 100 responses to our 'How did you hear about us?' question, sorted by timestamp descending.
NPS Trend Summary
@Fairing pull all NPS responses from the last 30 days, then calculate the score and summarize common detractor feedback.
Tag Open-Ended Feedback
@Fairing fetch the last 200 open-ended survey responses, then tag each one as product, shipping, pricing, or other.
Compare Campaign Periods
@Fairing get attribution responses from two weeks before and two weeks after our Black Friday campaign, then compare the channel mix.
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
@Fairing show me all active post-purchase survey questions we're running right now
Your shop has 4 active questions in rotation: (1) 'How did you hear about us?' shown to all first-time buyers; (2) 'What almost stopped you from buying?' shown to cart abandoners who returned; (3) 'Which product are you most excited to try?' shown after orders over $50; (4) 'Would you recommend us to a friend?' shown 7 days post-delivery. Each includes its targeting rules and response options. Question 1 has collected 1,847 responses in the last 30 days.
This example demonstrates the List Questions tool's ability to surface your entire survey configuration at once. Requires a valid Fairing API key with read access. Useful for auditing what customers see at different journey stages, but doesn't show response data—pair with a follow-up prompt to analyze answers.
@Fairing pull the last 100 responses to our 'How did you hear about us?' question from the past week, sorted by most recent first
Retrieved 100 responses from Dec 15-22. Top attribution sources: Instagram (34%), Friend referral (28%), Google search (19%), TikTok (11%), Other (8%). Most recent response 4 hours ago cited 'Instagram ad showing the holiday bundle'. The API returned response text, timestamps, and associated order IDs. Note: Fairing paginates at 100 responses per call; your question collected 340 total responses this week, so two more pages exist.
This example shows the List Responses tool filtering by question ID and date range. Requires knowing your question's internal ID (obtained from List Questions first). Pagination means large datasets need multiple calls—plan accordingly if analyzing thousands of responses. Response text is verbatim customer input, so expect typos and varied formatting.
@Fairing get all responses mentioning 'shipping' or 'delivery' from the last 30 days, then summarize the main complaints and how many people raised each issue
Found 67 responses containing shipping/delivery keywords across all questions. Main themes: (1) 'Took longer than expected' — 31 mentions, often citing 7-10 day waits vs. 3-5 day promise; (2) 'No tracking updates' — 18 mentions, customers wanted proactive notifications; (3) 'Damaged packaging' — 12 mentions, mostly international orders; (4) 'Expensive shipping cost' — 6 mentions, cart abandoners specifically. The API doesn't natively filter by keyword, so this required fetching all recent responses and scanning text client-side.
This synthesis example pairs Fairing's raw response data with AI text analysis to surface patterns. The MCP itself doesn't offer keyword search—you're retrieving all responses in a time window, then the AI scans them. Effective for qualitative insight mining, but token-heavy if your shop collects hundreds of daily responses. Consider narrowing the date range or question scope first.
Use-case deep-dives
When Fairing MCP closes the loop on qualitative metrics
A 4-person support team wants to pull Fairing NPS responses into Switchy to flag detractors during daily triage. The MCP can fetch responses with time filters, but it's read-only and doesn't expose customer contact info or order IDs in a structured way—you get survey answers, not a support ticket. If your workflow is 'read the feedback, then jump to Shopify or Gorgias to act on it,' the MCP adds a step instead of removing one. This integration shines when the goal is analysis (spotting trends, summarizing themes), not operational response. For teams that need to route negative feedback to a ticketing system within an hour, keep Fairing's native Slack or email alerts and use the MCP only for weekly retrospectives. The two-tool limit means you're not automating follow-up—just reading faster.
Frequently asked
What does the Fairing MCP do in Switchy?
It pulls survey data from your Fairing post-purchase surveys into Switchy's AI workspace. You can query response trends, filter by question or date range, and review targeting rules — all without leaving your team's chat. Useful for product, marketing, or CX teams who want quick access to customer feedback without logging into Fairing's dashboard every time.
Do I need admin access to connect Fairing?
You need an API key from Fairing, which typically requires account-owner or admin permissions to generate. Once you have the key, any Switchy workspace member can paste it into the MCP connection flow. If your Fairing account restricts API access, check with whoever manages your Fairing subscription first.
Can the Fairing MCP send new surveys or edit questions?
No. It's read-only. You can list questions and pull responses, but you can't create surveys, change targeting rules, or modify question text through Switchy. For setup or edits, you still need Fairing's web app. Think of this MCP as a reporting layer, not a configuration tool.
Why use this instead of exporting CSVs from Fairing?
Speed and context. Instead of downloading a spreadsheet, cleaning it, then pasting snippets into Slack or a doc, you ask Switchy's AI to summarize responses or flag trends in real time. The MCP keeps your team's conversation and the data in one place, so decisions happen faster.
Who on the team should connect the Fairing MCP?
Whoever owns customer insights or post-purchase analytics — usually someone in product, growth, or CX. They'll need the API key and should understand which surveys are live. Once connected, the whole workspace can query the data, but one person should own keeping the connection valid if the key rotates.