Data Studio

Data Studio Agent

Let your AI coding agent securely access all your databases, in plain language.

Local-first. Enterprise-grade security. Open source.

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This repository contains the Data Studio MCP Server, a Model Context Protocol server that gives AI coding agents direct access to your databases through the dockit and sqlkit desktop apps.

• SQL (via sqlkit): 70+ databases (PostgreSQL, MySQL, SQL Server, Oracle, SQLite, DuckDB, ClickHouse, Snowflake, BigQuery, and more)
• NoSQL (via dockit): Elasticsearch, OpenSearch, MongoDB, DynamoDB

Features

• Any AI coding agent. Claude Code, Cursor, Windsurf, OpenCode, Codex, Cline, Pi, Qoder, GitHub Copilot, or any MCP client.
• Any OS. macOS, Windows, Linux.
• Any LLM model. Bring your own provider. No lock-in.
• One MCP server, one config. Routes to both SqlKit (SQL) and DocKit (NoSQL) bridges over localhost.
• Enterprise-grade security. See below.

Quick start

1. Prerequisites

Install and launch dockit and/or sqlkit, add a database connection, and make sure Settings → MCP Bridge → Auto-start is enabled (it is by default). Install both apps for the full SQL + NoSQL tool set.

2. Install the MCP server

npm install -g @geek-fun/data-studio-mcp

Or run it without installing (npx downloads it on first run):

npx -y @geek-fun/data-studio-mcp

3. Add it to your AI tool

OpenAI Codex, one command:

codex mcp add data-studio -- npx -y @geek-fun/data-studio-mcp

Claude Code, one command:

claude mcp add --transport stdio data-studio -- npx -y @geek-fun/data-studio-mcp

Cursor. Create .cursor/mcp.json (project) or ~/.cursor/mcp.json (global):

{

"mcpServers": { "data-studio": { "command": "npx", "args": ["-y", "@geek-fun/data-studio-mcp"] } } }

Windsurf. Create ~/.codeium/windsurf/mcp_config.json (global only):

{

"mcpServers": { "data-studio": { "command": "npx", "args": ["-y", "@geek-fun/data-studio-mcp"] } } }

OpenCode. Add to opencode.json (project) or ~/.config/opencode/opencode.json (global):

{

"$schema": "https://opencode.ai/config.json", "mcp": { "data-studio": { "type": "local", "command": ["npx", "-y", "@geek-fun/data-studio-mcp"], "enabled": true } } }

DeepSeek Harness. Save the overlay as data-studio.cordis.yml:

- insert:

• id: data-studio-mcp name: '@deepseek-ai/dsh-mcp-client' config: serverName: data-studio transport: stdio command: npx args: ['-y', '@geek-fun/data-studio-mcp']

Run dsh web --patch "$PWD/data-studio.cordis.yml", or merge the insert patch into $DSH_HOME/cordis.patch.yml to keep it across runs. Tools appear as mcp__data-studio__* (DSH is developer preview — its config surface may change).

WorkBuddy / CodeBuddy CLI. One command:

codebuddy mcp add --scope user data-studio -- npx -y @geek-fun/data-studio-mcp

Or add the same mcpServers JSON shown for Cursor above to the project-level .mcp.json (~/.codebuddy/.mcp.json for user scope).

Any other MCP client. Register a stdio server with command npx and args -y @geek-fun/data-studio-mcp.

4. Tune permissions (optional)

Open Settings → MCP Bridge in dockit/sqlkit to control what the agent can do:

5. Start asking

Use plain language. The agent queries your databases for you:

• "List all tables in my PostgreSQL database"
• "Show me the last 10 orders from the Elasticsearch index orders*"
• "Find all users older than 30 in MongoDB"
• "Run this query and explain the results"

The agent reads schemas, runs queries, and explores your data, then shows you every query it executed.

Enterprise-grade security

The LLM gets broad access to your data, but it never sees your credentials. The policy model gates every capability by risk level.

• Credentials never leave the apps. The LLM only ever sees an opaque connection_id. Real credentials are resolved inside dockit/sqlkit and never cross the MCP boundary. Your passwords and keys stay on your machine, in your app.
• ID-based resource access. Agents access databases strictly by connection ID. Credentials never appear in prompts or tool arguments, so there is no path for the model to obtain or exfiltrate connection secrets.
• Three-tier permission model. Read Only / Data Read-Write / Full Access modes gate every capability by risk level, with per-connection overrides. You can mark any connection read-only or allowlist specific actions.
• Explicit user confirmation. Destructive operations (DELETE, DROP, TRUNCATE) surface as Ask in the policy. The client prompts the user for explicit confirmation before anything destructive runs.
• Action-level statement classification. SQL is parsed and classified by statement kind (Read / Write / Delete / DDL) before execution. Write-only tools reject DELETE statements; delete tools reject DDL.
• Local-only bridge. The bridge binds to 127.0.0.1 exclusively. It is unreachable from other machines, with no server to host and no API keys to manage.

Tools

All tools follow the data_studio__{app}__{backend}__{action} convention: app is the desktop app exposing the bridge (sqlkit or dockit), and backend is the data source behind it (sqlkit, es, mongo, dynamo). The two server-level tools (data_studio__list_connections, data_studio__get_status) have no app segment. The User confirmation column shows which operations surface an explicit confirmation prompt in your AI client before they run.

The exact tool set advertised at runtime depends on your apps' permission mode and configured connections — call data_studio__get_status to see what is currently available.

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