Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@FrancoLabs MCP Server create a study for the new onboarding experience"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Connect AI assistants to FrancoLabs — a UX research platform — over the Model Context Protocol. Let Claude, ChatGPT and other MCP clients create and manage studies, browse templates, read participant responses and analytics, run AI-grounded analysis, and turn insights into product opportunities on your behalf.
Endpoint: https://francolabs.vercel.app/mcp
Transport: Streamable HTTP (MCP protocol 2025-06-18)
Auth: OAuth 2.1 (Dynamic Client Registration + PKCE) or Personal Access Token
Registry: io.github.glauberfrancodesign/francolabs
Settings → Connectors → Add custom connector → URL:
https://francolabs.vercel.app/mcpComplete the OAuth consent screen — you choose exactly which scopes to grant and can toggle read-only access.
Enable Developer Mode, then Settings → Connectors → Add → same URL. Authenticate via the OAuth flow.
Generate a Personal Access Token in Settings → Integrations, then:
claude mcp add --transport http francolabs \
"https://francolabs.vercel.app/mcp" \
--header "Authorization: Bearer <your-token>"Related MCP server: AI Survey Creator MCP Server
A tool listed with two scopes needs both. AI marks tools that call a language model and use AI credits; the model only sees the deterministic digest and curated insights, with e-mail addresses and phone numbers masked first.
Tool | Scope | Description |
|
| Question/block types with the exact settings shape and a valid example |
|
| Study templates, optionally by category |
|
| Workspaces (teams) you belong to and your role in each |
|
| Your projects, optionally by status |
|
| A single project by id |
|
| The workspace plan and current usage: limits, what is enforced now, grants and when extended access ends, and the per-person AI-create cap |
|
| Create a project (optionally in a specific workspace) |
Tool | Scope | Description |
|
| Studies, filtered by status and/or project |
|
| A full study with blocks and settings |
|
| Create a Draft study; blocks are validated against |
|
| Update title, blocks, project; |
|
| Launch a Draft, pause, resume a Paused study, or complete it |
|
| Permanently delete a study and its responses |
|
| AI — turn a research objective into a typed study skeleton |
|
| AI — survey-design review with a quality score per block |
Tool | Scope | Description |
|
| Raw participant responses, newest first — may contain personal data written by respondents; pass |
|
| Aggregated KPIs: responses, completion, duration, NPS, distributions |
|
| Deterministic, PII-free statistical digest for every question type |
|
| AI — run/refresh the full analysis (replaces non-pinned AI insights) |
|
| AI — ask a question grounded in the digest and cached analysis |
|
| Curated insight cards, pinned first |
|
| Create or edit a curated insight card |
|
| Record 👍/👎 feedback on an insight |
|
| AI — tag insights with a consistent product taxonomy |
|
| Replace the tags of one insight |
|
| AI — cross-study question answered from past curated insights |
Tool | Scope | Description |
|
| Product opportunities distilled for a study |
|
| AI — distill insights into draft opportunities |
|
| AI — 3–5 follow-up studies that de-risk open opportunities |
|
| AI — stakeholder-ready decision package (Markdown) |
|
| Decision packages already generated for a study |
|
| Export an opportunity to Linear as an issue — internal users only |
|
| Linear issues already created from a study's opportunities |
Tool | Scope | Description |
|
| Respondent contacts of the workspace — personal data (e-mail, name, locale/country, research consent, tags, response counts) |
|
| One contact with the studies they answered — personal data |
|
| Add/remove tags on contacts to segment respondents — never put sensitive information in tags |
create_project and create_study write into a workspace — pass workspaceId
(see list_workspaces), or omit it to use your oldest workspace by default.
Write tools also require an editor role (owner, admin or member) in the study's
workspace; viewers can only read.
Two resources are also exposed: francolabs://templates and
francolabs://question-types.
templates:read · projects:read · projects:write · studies:read ·
studies:write · responses:read · contacts:read · contacts:write
contacts:read and contacts:write give access to personal data of your
respondents (e-mail, name, locale/country, research consent, tags and response
history). They are opt-in: the consent screen and the Read-only token preset
leave them unchecked; grant them only to assistants that need your contacts base.
responses:read returns raw answers, which can also contain personal data typed by
respondents.
Tools you didn't grant are hidden from tools/list. A read-only grant blocks
every write tool server-side, regardless of scopes.
Tokens are stored only as SHA-256 hashes — never in plaintext.
Each request mints a short-lived (5 min) JWT so all data access runs under per-user row-level security; the service role is used only for the token lookup.
OAuth access and refresh tokens last 30 days; a connection never outlives 90 days. Access is revocable anytime from Settings → Integrations, and clients can revoke a token at the revocation endpoint (RFC 7009).
OAuth follows RFC 9728 (protected-resource discovery), RFC 8414 (AS metadata), RFC 7591 (dynamic client registration) and PKCE S256.
This repository mirrors the server's source for transparency. The server runs as
a Supabase Edge Function (src/mcp, src/mcp-oauth, src/_shared).
© FrancoLabs. All rights reserved.
This server cannot be deployed
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