Guided onboarding
Three steps, no config hunting.
Users create a key, paste one adaptive setup prompt into their AI agent, then confirm the MCP server is connected. The technical snippets stay available below for fallback.
Login and create your MCP key
Open DataUnmess in a new tab, sign in, and create a workspace MCP key. The key is free during beta and shown once.
Copy one prompt into your AI
Use the same adaptive prompt for every AI agent. After the config is saved, restart or reload the AI client so it discovers the new MCP server.
Universal setup prompt
Works for Codex, Claude, Cursor, Windsurf, Gemini, ChatGPT, and other MCP-capable clients.
Preview prompt
I already have a DataUnmess MCP key. Install the DataUnmess MCP server into this AI client.
Use this endpoint: https://app.dataunmess.ai/api/mcp
First detect which client/environment you are running in.
If this is Claude Code (the CLI / VS Code extension), do NOT hand-edit ~/.claude.json. Run this command exactly — the name and URL are positional and must come before the flags:
claude mcp add dataunmess https://app.dataunmess.ai/api/mcp --transport http --scope user --header "Authorization: Bearer <YOUR_MCP_KEY>"
If this is Codex Desktop, use the TOML config below. If this is Claude Desktop, Cursor, Windsurf, ChatGPT, Gemini, or another remote-MCP JSON client, use that client's native MCP JSON config. If you are unsure which config file to edit, fetch https://app.dataunmess.ai/connect-mcp for the current snippets or ask me which client I am using before editing.
For Codex Desktop, the config block is:
[mcp_servers.dataunmess]
url = "https://app.dataunmess.ai/api/mcp"
http_headers = { Authorization = "Bearer <YOUR_MCP_KEY>" }
For JSON-style remote MCP clients, the config entry is:
{
"mcpServers": {
"dataunmess": {
"url": "https://app.dataunmess.ai/api/mcp",
"headers": {
"Authorization": "Bearer <YOUR_MCP_KEY>"
}
}
}
}
Keep the literal string <YOUR_MCP_KEY> as the bearer token. Keeping the real key out of this chat is intentional; I will paste it into the local config file (or the `claude mcp add` command) myself after you save it.
After saving, remind me to replace <YOUR_MCP_KEY> with my real key, restart or reload the AI client, wait up to 60 seconds if the MCP server list does not refresh immediately, then test with: "Start DataUnmess Memory onboarding. Ask me for my company website, research public pages, and seed DataUnmess Memory markdown with company context, products, glossary, standard KPIs, and useful business terms. Then propose three dashboards we should create first.".
If the AI can start that onboarding flow with DataUnmess MCP tools, the connection is working.Check if MCP is connected
Replace the placeholder with your real key, restart or reload the AI client, then paste the onboarding prompt below. If the AI starts the flow, MCP is connected.
Paste this in the AI client after restart. It tests the MCP connection and starts the recommended onboarding flow.
Recommended next steps
Start with one of these recommended steps.
Once the setup is done, use one starter ask. We recommend starting with DataUnmess Memory onboarding so future MCP answers, research, dashboards, flowcharts, and data pipelines can reuse your company and KPI memory.
Seed DataUnmess Memory
Copy this after the setup shows Done.
Preview prompt
Start DataUnmess Memory onboarding. Ask me for my company website, research public pages, and seed DataUnmess Memory markdown with company context, products, glossary, standard KPIs, and useful business terms. Then propose three dashboards we should create first.Create a dashboard from a database or file
Copy this after the setup shows Done.
Preview prompt
Use DataUnmess.ai MCP to create a dashboard from my database or a file. Ask me which source to use, inspect the schema or columns, then build KPI cards, a trend chart, and the most useful breakdowns.Propose new insights and dashboards
Copy this after the setup shows Done.
Preview prompt
Use DataUnmess.ai MCP to review what you know about my company and available data, then propose new insights and dashboards that could be created. Group the ideas by business value and effort.Manual config and advanced setup
Use this only if you want to edit the client config yourself. Replace <YOUR_MCP_KEY> with the key from step 1, then restart or reload your AI client.
Claude Code (CLI / VS Code extension)
Config file:
- All OS: ~/.claude.json (mutated by the CLI)
Run this command (name and URL are positional and must come BEFORE the flags):
claude mcp add dataunmess https://app.dataunmess.ai/api/mcp --transport http --scope user --header "Authorization: Bearer <YOUR_MCP_KEY>"Codex Desktop
Config file:
- macOS/Linux: ~/.codex/config.toml
- Windows: %USERPROFILE%\.codex\config.toml
Add this TOML block:
[mcp_servers.dataunmess]
url = "https://app.dataunmess.ai/api/mcp"
http_headers = { Authorization = "Bearer <YOUR_MCP_KEY>" }Remote MCP JSON clients
Config file:
- Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json
- Cursor: ~/.cursor/mcp.json
- Windsurf: ~/.codeium/windsurf/mcp_config.json
Add this entry:
{
"mcpServers": {
"dataunmess": {
"url": "https://app.dataunmess.ai/api/mcp",
"headers": {
"Authorization": "Bearer <YOUR_MCP_KEY>"
}
}
}
}Endpoint: https://app.dataunmess.ai/api/mcp. Codex Desktop can take up to 60 seconds to repaint Settings > MCP after config changes.
query_dataFilter, group, and aggregate datasets (CSV, Excel, uploads). Returns result rows as JSON.
build_chartGenerate a chart spec from queried data. 22 chart types: bar, line, donut, scatter, treemap, funnel, radar, sankey, heatmap, waterfall, gantt, and more.
build_analysisCreate an analysis panel with text summary and KPI highlight cards showing metrics and trends.
query_connectionExecute a SELECT SQL query against a connected database (PostgreSQL, MySQL). Read-only — write queries are blocked.
query_google_sheetsRead a connected Google Sheets / Drive source. Actions: list_spreadsheets, get_metadata, read_range, read_sheet_as_table. Read-only OAuth scopes.
build_flowchartBuild a flowchart with 8 node shapes (rect, diamond, ellipse, hexagon, parallelogram, cylinder, cloud, document), optional lucide icons per node, and labeled edges. Decision diamonds with multiple outgoing edges auto-fan into parallel lanes. Accepts structured nodes+edges JSON or a Mermaid `flowchart TD|LR` source block.
Four resources are also exposed: dash-ai://datasets, dash-ai://datasets/{id}, dash-ai://connections, and dash-ai://connections/{id}. Read dash-ai://connections first to discover available data sources before calling query_* tools.