tableau-embed
Embeds live, interactive Tableau dashboards in ChatGPT, pushing mark selections to the model so questions about specific data points are answered with live queried numbers.
README
Tableau MCP App
Render a live, interactive Tableau dashboard inside ChatGPT, click a mark, and ask about what you clicked.
Not a screenshot and not a link out. The real viz, embedded in the conversation, filterable and hoverable — and when you select something, the selection is pushed to the model so that "why is this one so low?" resolves to the mark under your cursor.
You click Fasteners on the dashboard.
Fasteners selected on "KPI by Sub-Category" - nothing else is filtered.
Mark: Sub-Category: Fasteners | AGG(KPI Value): 8,532 | AGG(KPI Label): $8.5K
Parameters: Profit Bin Size = 200 | Top Customers = 5 | p.KPI = Sales
You: "why is this one so low?"
ChatGPT: "Sales: $8,532, lowest of 17 sub-categories. Average line sale: $37.26...
980 units across 226 orders. Average discount 7.9%, so discounting is
not the main cause. Profit: $2,429, a healthy 28.5% margin."
Those numbers are queried live from the Tableau data source, not invented — verified against the raw query response, which is a distinction worth insisting on.
What this actually is
An MCP Apps server. It exposes one tool that
returns a ui:// resource — an HTML view the host mounts in a sandboxed iframe — which loads the
Tableau Embedding API, renders your viz, and reports the on-screen state back to the model as the
user interacts with it.
It deliberately returns no data of its own. The embed supplies the scope; a Tableau data query tool supplies the numbers. See How it works for why that split is the right one.
Status, honestly
| ChatGPT | Works. Renders, interacts, and pushes state; a selected mark was verified to scope the answer against a live query. You have to name the connector when asking — see Invoking it. |
| Claude Desktop | Renders nothing. It approves the frameDomains CSP declaration and then blocks frame-src anyway — a host defect no server-side change can work around. Details. |
| Tableau Public | Works anonymously, no credentials needed. |
| Tableau Cloud | Works, via a Direct Trust Connected App JWT signed server-side. |
This is a working prototype built to answer a question — can you have a real conversation with a dashboard you're looking at? — and the answer turned out to be yes, with caveats worth reading.
Invoking it
Name the connector:
"Use the tableau-embed connector to show me the Superstore Performance dashboard"
This is the documented invocation, not a workaround for a bug. Asked without naming it, "show me the dashboard" reliably loses to ChatGPT's own chart builder, which will happily construct a convincing dashboard out of sample data and present it as an answer. Three rewrites of the tool description failed to change that. Once the dashboard is on screen, ordinary questions work normally — you only need to name the connector for the initial render.
Quickstart
Requires Node 18+ and, for the Cloud path, a Tableau Connected App.
git clone https://github.com/nuggenlabs/tableau-chatgpt-embed.git
cd tableau-chatgpt-embed
npm install
npm test # proves the viz renders under the real sandbox CSP, headlessly
npm test is worth running before anything else. It reproduces the MCP Apps iframe sandbox and
its Content Security Policy locally, drives a real Tableau viz through it in headless Chrome, and
tells you which of the four layers fails if one does. It also drives a real mark selection and
asserts the selection reaches the host — so a broken state push fails the build rather than
surfacing as a confused answer three weeks later.
To run it against a dashboard and connect it to ChatGPT:
cp .env.example .env # fill in, or delete the four credential lines for Tableau Public
npm start
Then expose it over HTTPS and register the /mcp URL as a connector. On Windows, run.ps1 does
the whole sequence — server, tunnel, health check, paste-ready URL:
powershell -ExecutionPolicy Bypass -File .\run.ps1
Full walkthrough, including the credential traps: docs/SETUP.md.
Documentation
| Document | What's in it |
|---|---|
| Setup | Credentials, tunnels, the startup preflight, and getting it into ChatGPT |
| How it works | Architecture, how on-screen state reaches the model, mark selection |
| Host support | What works where, and the Claude Desktop frame-src block in detail |
| Troubleshooting | Failures that cost real time here, and what each one actually was |
| Scoping test | A protocol for testing whether the model uses on-screen state |
Two findings worth stealing
A tool description is a routing instruction — and it can lose. "Show me the Superstore dashboard" was answered by ChatGPT building a plausible lookalike from a sample dataset — invented numbers, presented as an answer. Three successive rewrites of the description failed to win the request back, including one that names the competing behaviour and refuses the substitution outright. A tool competes with everything the host can do, not just its siblings in the same server, and against a first-party tool a description may simply not be enough. Unresolved — name the connector, as below.
Ask the harness, not the model. A model is not a reliable instrument for measuring its own
context. When on-screen state stopped reaching ChatGPT, four plausible theories each blamed the
wrong layer; what settled it was planting a random token in every push and asking for it back —
thirteen pushes, every call resolved, none delivered. docs/SCOPING-TEST.md is the general form of
that lesson.
Layout
src/
server.js MCP server: the tool, the ui:// resource, HTTP + stdio transports
view.html the embedded view - renders the viz, reads state, pushes it to the model
build-view.js inlines the MCP Apps client SDK into the view at build time
connected-app.js Direct Trust JWT minting for authenticated Cloud embeds
preflight.js startup credential and URL checks, so failures surface here not in ChatGPT
env.js dependency-light .env loader; shell variables win over the file
test/
sandbox-harness.js reproduces the host: the iframe sandbox, the CSP, the ui/ JSON-RPC contract
feasibility.test.js end-to-end - viz renders, host contract satisfied, state reaches the model
server.smoke.js MCP protocol correctness
preflight.test.js credential-failure classification, stubbed
env.test.js .env parsing and precedence
docs/ see the table above
run.ps1 Windows launcher: server + tunnel + health + paste-ready URL
No build step and three runtime dependencies. The Tableau Embedding API is loaded by the view at runtime from Tableau's own CDN, which the sandbox CSP has to allow — that constraint drives more of the design than anything else.
Not yet built
- Choosing a dashboard by name. One viz is baked in at startup via
TABLEAU_VIZ_URL. Switching conflicts with the one-embed-per-conversation rule, which exists because a second embed mounts a fresh unfiltered viz and silently resets the reported scope. - Inbound authentication.
/mcpis open to anyone who can reach it, andresources/readmints an embed token on demand. Fine behind an ephemeral tunnel; do not put this on a permanent public hostname without putting something in front of it.
License
MIT — see LICENSE.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
E2B
Using MCP to run code via e2b.