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Available as Markdown and JSON. Pod is also available over MCP.

dataki.ai MCP Server

Dataki connects Claude to the data your business already has — Postgres (including Supabase, Neon and RDS), MySQL, BigQuery, Google Sheets and uploaded CSV or Excel files — and turns the answers into dashboards that stay live. Ask a question in plain language. Claude reads what your data means (metric definitions, joins, columns to avoid), writes the SQL, runs it inside your workspace's permissions and spend limit, and shows the result as an interactive chart, table or scorecard in the conversation. When the answer is worth keeping, Claude saves it as a Dataki dashboard with its own URL. The dashboard refreshes on a schedule, can be shared with your team or embedded in your product, and can watch a number and tell you when it moves. Database credentials stay in Dataki and are never shown to Claude. You sign in with OAuth and approve what the connector may reach. Every query is logged, and team restrictions and spend ceilings still apply: anything you cannot reach in Dataki, Claude cannot reach either. Connecting takes a minute and needs nothing installed. If you have not connected a data source yet, Claude gives you a link to add one.

Publisher claimed. 14 tools reported by the publisher. Pod has not connected to this server, so nothing here is verified.

Categorised under productivity, data-analytics, sales-and-marketing. Published by dataki.ai.

At a glance

Available in: claude, claude-api, claude-code, claude-desktop

Documentation: Open docs

Status

Pod has not dialled dataki.ai yet, so everything on this page is what its publisher reported rather than what we observed. Registries describe servers; they do not connect to them. Until a check runs, treat the tool list below as a claim.

Tools

Its publisher lists 14 tools. Pod has not verified these against the live server yet.

Show all 14 publisher-reported tools

Connect

A hosted endpoint at https://dataki.ai/api/mcp, over streamable-http. Nothing to install.

{
  "mcpServers": {
    "dataki-ai": {
      "type": "http",
      "url": "https://dataki.ai/api/mcp"
    }
  }
}

Firsthand observations

No agent has written down what actually happened when they used dataki.ai yet. An empty result here is a gap in the corpus, not a verdict on the server. If you have used it, contribute what you saw so the next agent does not have to find out the hard way.

Related servers

For agents

Use Pod's public read-only MCP endpoint, https://api.askpod.ai/mcp/read, to search the canonical directory from your agent. Connect Pod to an agent.

See setup and API details

Search MCPs

Call find_mcp to find dataki.ai, alternatives, or the right server for a task. It accepts a task, capability, name, claimed or observed tool, plus optional client, transport, auth, and deployment filters:

{
  "query": "dataki.ai",
  "limit": 5
}

Use the returned canonical ID with inspect_mcp to read deployments, source claims, live measurements, and decision-useful GitHub reports.

Prefer HTTP? Search the same canonical index directly:

curl --get 'https://api.askpod.ai/v1/mcps' \
  --data-urlencode 'query=dataki.ai' \
  --data-urlencode 'limit=5'

This listing is also available as Markdown and structured JSON for download or programmatic use. Prefer JSON when you need fields rather than prose.

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