datatomcp

com.datatomcp由 datatomcp.com 发布官方发布者v1.3.0更新于 Nov 5, 2026

AI-native data layer: sheets, databases and apps for every agent, with exact answers and audit.

已验证Streamable HTTP可网页运行数据库文件与存储数据与分析

概览

AI 生成的概览

托管式数据层,让 AI 助手查询团队的表格、文件和数据库,返回精确、带版本的结果并保留审计记录。

可以做什么
  • 浏览数据集:列出数据集及行数与新鲜度,查看列、类型、定义与关联,获取工作区总览
  • 查询数据:筛选、排序、分页读取行,按列分组做计数、求和、平均等聚合,列出列的真实取值
  • 可视化:在对话中把查询结果绘制成图表
  • 仪表盘:创建、列出并刷新工作区中的仪表盘
  • 向团队发送数据问题说明
适用场景
适合数据分散在 Google Sheets、CSV 或 Excel 文件、PostgreSQL、MySQL、Stripe、HubSpot、Airtable 中、又没有专职数据团队的场景。当需要精确汇总、共享指标定义,并记录智能体问过什么时尤其合适。
运行要求
远程 streamable HTTP 端点,无需安装。需要账号并通过 OAuth 登录(支持动态客户端注册),授权可随时撤销。需先接入数据源:上传文件、在 Google 选择器中指定表格,或连接 PostgreSQL、MySQL、Stripe、HubSpot、Airtable。仅支持本地服务器的客户端需借助 mcp-remote 之类的桥接。免费版有额度限制,Pro 为付费。
安装前请注意
智能体可读取已接入的数据,因此这些数据集中的内容会暴露给助手;每个连接可限制在部分工作区。写入类工具会创建仪表盘或向团队发送说明,从而改动共享工作区内容。数据托管在欧盟,声明不用于训练。内置 AI 功能默认关闭,需手动开启。更高额度需付费 Pro 方案。

安装

在 SourceWeft 中

  1. 打开 控制台中的 datatomcp,将其添加到工作区。
  2. 连接 所需的 凭据。它们会被私密存储,绝不会在公开页面上展示。
  3. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/com.datatomcp%2Fdatatomcp/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。 客户端会在首次连接时完成 OAuth 登录。

{
  "mcpServers": {
    "datatomcp": {
      "type": "http",
      "url": "https://app.datatomcp.com/mcp"
    }
  }
}

README

[datatomcp]

The AI-native data layer for teams without a data team. Your team's data, ready for every AI agent.

Website · Start free · Guides · Demo video

[Listed on mcpservers.org]


datatomcp is one governed place for a team's data, that every AI agent can ask: Google Sheets, CSV and Excel files, PostgreSQL and MySQL, Stripe, HubSpot and Airtable. Connect your data once: datatomcp links the datasets, drafts definitions of what each number means for a person to approve, and records every question an agent asks. Agents (Claude, ChatGPT, Cursor, n8n and others) query it over MCP or REST, and a database computes the answer, so totals are exact. News: datatomcp is now an AI-native data layer (4 Nov 2026).

  • Hosted. Nothing to install or run. One URL works in every client that supports remote MCP servers.
  • OAuth sign-in. No API key to paste into your agent. You can revoke access at any time.
  • Exact, versioned results. Counts, sums and averages come from DuckDB. Every result names the dataset version it came from and links to the rows behind it.
  • Shared definitions. Approved definitions like paid_revenue and a glossary of your team's words. Agents use them by name, and the answer says who approved the definition.
  • Audit trail. Every agent question is kept: who asked, from which agent, the query, the data version. Repeated questions are compared. Export as CSV.
  • Linked datasets. Orders, customers and regions in one question, with unmatched rows counted.
  • Charts and dashboards. The chart tool draws the answer in the chat (52 chart types, as an MCP Apps view). Agents make dashboards from your questions and refresh them later; dashboards never change your data.
  • Workspaces. Owners, editors and viewers; each agent connection can be limited to some workspaces.
  • Private Google Sheets. You pick the sheet in Google's file picker, and datatomcp can read that file only. Pro syncs within 5 minutes of an edit.
  • Import report. Bad lines, numbers stored as text and mixed date formats are reported before any agent sees the data.
  • Read-only data. Agents can query your data, but they can't change it or run raw SQL. The only tools that write create dashboards or send your team a note.
  • Hosted in the EU. Your data is never used for training. AI features inside datatomcp (cleaning suggestions, an assistant on your own key) are off until you turn them on.

This repository holds the public documentation and client configuration. The service itself runs at app.datatomcp.com.

Server

URLhttps://app.datatomcp.com/mcp
TransportStreamable HTTP
AuthOAuth 2.1 (dynamic client registration and client ID metadata documents)
Registry namecom.datatomcp/datatomcp

Quick start

  1. Sign up at app.datatomcp.com (free, no card).
  2. Add a dataset: upload a CSV or Excel file, choose a Google Sheet, or connect PostgreSQL, MySQL, Stripe, HubSpot or Airtable. New accounts start with an example dataset.
  3. Add the server to your client (below), sign in when asked, and ask a question about your data.

Tools

ToolWhat it does
workspace_overviewEvery dataset with what its columns mean, the approved definitions, the links between datasets and example questions, in one call
list_datasetsYour datasets, with row counts and freshness
describe_datasetColumns, types, example values, approved definitions, glossary, links to other datasets, data warnings and today's date
query_rowsFilter, sort, select columns and paginate (up to 1,000 rows per call)
aggregateGroup by columns; count, distinct count, sum, average, min and max, or a saved definition by name
distinct_valuesThe real values in a column, so filters match exactly
chartDraws a query's answer as a chart in the conversation, with the takeaway as the title
create_dashboardMakes a dashboard (a one-page board or a short story of slides) in your workspace
list_dashboardsThe workspace's dashboards
refresh_dashboardRe-runs a dashboard on the current data
report_issueSends your team a note about a data problem

Filters support eq, neq, gt, gte, lt, lte, in, not_in, contains, starts_with, between, is_null and not_null.

Client setup

Claude (Claude.ai and Claude Desktop)

Settings → Connectors → Add custom connector. Paste https://app.datatomcp.com/mcp, name it datatomcp, connect, and allow access.

Claude Code

bash
claude mcp add --transport http datatomcp https://app.datatomcp.com/mcp

Then run /mcp, choose datatomcp and sign in.

ChatGPT

Settings → Apps & Connectors → Advanced, and turn on developer mode. Create a connector with the URL above and OAuth authentication, then sign in.

Cursor

Settings → MCP → Add new server. Choose type HTTP and use the URL above. Cursor asks you to log in the first time.

VS Code

.vscode/mcp.json:

json
{  "servers": {    "datatomcp": { "type": "http", "url": "https://app.datatomcp.com/mcp" }  }}

Windsurf

~/.codeium/windsurf/mcp_config.json:

json
{  "mcpServers": {    "datatomcp": { "serverUrl": "https://app.datatomcp.com/mcp" }  }}

Cline

Open the MCP Servers panel → Remote Servers, name it datatomcp, and use the URL above. Or add it to cline_mcp_settings.json:

json
{  "mcpServers": {    "datatomcp": { "type": "streamableHttp", "url": "https://app.datatomcp.com/mcp" }  }}

Antigravity

Customizations → MCP servers → View raw config (mcp_config.json):

json
{  "mcpServers": {    "datatomcp": { "serverUrl": "https://app.datatomcp.com/mcp" }  }}

Click Refresh, then Authenticate.

OpenCode

opencode.json:

json
{  "$schema": "https://opencode.ai/config.json",  "mcp": {    "datatomcp": { "type": "remote", "url": "https://app.datatomcp.com/mcp" }  }}

Codex

bash
codex mcp add datatomcp --url https://app.datatomcp.com/mcpcodex mcp login datatomcp

Gemini CLI

bash
gemini mcp add --transport http datatomcp https://app.datatomcp.com/mcp

Then run /mcp auth datatomcp.

Clients with local (stdio) servers only

Use mcp-remote as a bridge. It handles the sign-in:

json
{  "mcpServers": {    "datatomcp": { "command": "npx", "args": ["-y", "mcp-remote", "https://app.datatomcp.com/mcp"] }  }}

Example

How many orders came from France in September, and what was the revenue?

The agent calls describe_dataset to learn the columns, then one aggregate call:

json
{  "dataset_id": "ds_orders",  "where": [    { "column": "country", "op": "eq", "value": "France" },    { "column": "order_date", "op": "between", "value": ["2026-09-01", "2026-09-30"] }  ],  "metrics": [    { "fn": "count", "as": "orders" },    { "fn": "sum", "column": "total", "as": "revenue" }  ]}

On our 50,000-row test sheet, it answers with 277 orders and €50,279.50 in revenue. The tool result is 32 bytes, not the whole sheet.

REST API

The same datasets, definitions and query engine are available over REST with an API key, for apps and automations such as n8n, Make or Zapier. Reference: datatomcp.com/docs/api. OpenAPI 3.1: datatomcp.com/openapi.json.

Plans

FreePro ($12 or €12 a month)
Datasets325
Rows per dataset100,0002,000,000
Queries per day1,00050,000
Workspaces1, 2 people3, 5 people each
Audit history30 days90 days
Google Sheets syncOn demandWithin 5 minutes

Guides

Support

Questions, bugs or a file that doesn't import the way you expect: [email protected], or open an issue in this repository.

License

The documentation and configuration in this repository are MIT licensed. The datatomcp service is proprietary.

来源:README.md,提交 e0605be

工具

0
该服务器需要登录,因此 SourceWeft 无法在没有账号的情况下读取其工具,也未收录。已连接账号所能看到的内容可能因账号及其权限而异。

版本历史

2
  1. v1.3.0最新Nov 5, 2026
  2. v1.0.0Oct 21, 2026

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