原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Sancho Fetch

作者:panth-net版本 0.3.1i★ –i在你的电脑上运行
注册名称io.github.panth-net/sancho-fetch

安装

概览

作者提供的说明(英文原文)

Local-first public-data MCP server that writes inspectable records and working files.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 文档、表格与办公

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

下载作者的 .mcpb 文件

链接指向作者自己的文件;我们不托管也不重新打包它。打开文件后,Claude Desktop 会进行安装。

在终端中运行这条命令

claude mcp add sancho-fetch -- uvx sancho-fetch mcp serve --workspace --transport stdio

这个服务器需要在启动命令中填入值,请复制设置后自行填写。

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "sancho-fetch": {
      "command": "uvx",
      "args": [
        "sancho-fetch",
        "mcp",
        "serve",
        "--workspace",
        "--transport",
        "stdio"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器需要在启动命令中填入值,请复制设置后自行填写。

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "sancho-fetch": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "sancho-fetch",
        "mcp",
        "serve",
        "--workspace",
        "--transport",
        "stdio"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 panth-net 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“文档、表格与办公”中的其他服务器比较i

  • 星标数多于此分类中其他 3542 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 3542 个服务器的 38% 更新
  • 基础检查结果等于或优于此分类中其他 3542 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Sancho Fetch 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.3.1)于2个月前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv), MCPB。

有什么可以替代它?

同一分类中有:Agent-Native Calendar, Excel MCP Server, MCP Server for Excel

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Agent-Native Calendar

作者:BuilderIO

Agent-Native Google Calendar - manage events, sync, and public booking

在云端运行★ 7,620MIT2个月前更新
安装

Excel MCP Server

作者:haris-musa

Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.

在你的电脑上运行★ 4,276MIT7天前更新
安装

MCP Server for Excel

作者:sbroenne

Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.

在你的电脑上运行★ 847MIT4天前更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Sancho Fetch

Ask your AI for public data. Get real files on your computer.

The fastest way in: tell your AI

Open your AI's code mode -- Claude desktop app -> Code tab, or ChatGPT desktop app -> Codex -- pick any folder, and paste this:

Install Sancho Fetch from PyPI (uv tool install sancho-fetch) and set it

up on this computer. If uv isn't installed, tell me, then install it

first. When you're done, tell me whether the install succeeded. Then

explain in plain English, no jargon: what the .env file is, how to use it,

where it is on my computer, and open it for me so I can paste API keys

straight into it. Give me a few examples of data sources that need a free

API key and a few examples of sources that don't need a key. Tell me where

my downloaded data gets saved, and how I update Sancho later. Then run a

short example data fetch using the World Bank so I can see where the data

was saved. Finally tell me a few natural language examples I can tell you

to fetch data so I can get a feel for how to use sancho-fetch and remind

me that 1) I can use it anywhere as long as I'm in a LLM Desktop app's

Code tab (e.g. ChatGPT Desktop or Claude Desktop), and 2) that I can ask

AI to add new data sources by itself since sancho-fetch is built in a way

that it's easily extensible (aka you can add more to it and the AI will do

the heavy lifting and connect the new data source).

That's the whole install.

You do not need to download or clone this repository. The two commands the AI runs are:

uv tool install sancho-fetch

sancho setup

Install once, use everywhere. Sancho installs once on your computer and connects to the AI apps it finds (Claude Desktop, ChatGPT/Codex, VS Code, Cursor). At the end of setup it tells you plainly which apps are ready and which just need a restart or a click.

To use Sancho, just ask for data and mention "sancho" in plain English, or type `/sancho` in Claude Code to feel cool.

What it does

  • In your AI's Code mode you ask: *"get county-level asthma rates for Ohio."* You can be working in any folder -- Sancho always knows where its home folder is.
  • Your AI uses Sancho to pull from 120+ public sources (Census, World Bank, FDA, USGS, FEMA, ...) and hands you a file that opens in Excel.
  • Everything is saved on your computer.

Before you begin

The normal prerequisite is uv. uv installs Sancho in isolation and can obtain a compatible Python without replacing your computer's system Python. First setup needs an internet connection. API keys are not required for installation or the World Bank example; provider-specific keys can be added later.

Get started (about 2 minutes)

Install straight from PyPI.

1. Open your AI's code mode.

  • Claude: Claude desktop app -> Code tab -> new session -> pick any folder where you want your data to live.
  • ChatGPT: ChatGPT desktop app -> Codex -> open that folder.

2. Paste the install request from the top of this page.

The AI installs everything, shows you your first data file, and walks you through where everything lives.

3. Restart the apps Sancho asks you to restart. Setup ends with a short report; if an app needs a restart or one extra click, it says so in plain words.

NOTE: Before using, make sure to add API keys for any sources you want to use.

Many sources need no key (World Bank, USGS, FEMA, openFDA, ...). Your AI will tell you when an API key is needed. You can either:

  1. Open the .env file in the sancho-workspace folder and paste your key there; or
  2. Ask your AI to open the keys file for you (sancho env open). Paste the key into the opened file, **don't paste your key

into the chat**.

Setup normally creates .env from .env.example. If .env is missing, sancho env open creates and opens it. To do this manually, copy .env.example in the same folder, keep the original template, and name the copy .env.

Both files may be hidden:

  • macOS Finder: press Cmd+Shift+. (Command + Shift + period).
  • Windows File Explorer: select View -> Show -> Hidden items.

@Developers, fyi Sancho is designed to read key names only, not values.

Prefer the GitHub repository, or installing without an AI app?

For contributing, custom module development, or offline installs, work from the repository folder instead. Install GitHub Desktop (free), then on this repository's page click Code -> Open with GitHub Desktop -> Clone.

Note: Avoid placing the Github folder in a folder that is within iCloud/OneDrive (e.g. Documents folder in iCloud). This causes issues with file syncing and speed.

Then double-click installers/Install Sancho.command (macOS) or installers/Install Sancho.bat (Windows); on Linux run bash installers/setup.sh.

  • macOS blocks the first open (the installer is not notarized): System Settings -> Privacy & Security -> scroll to the blocked-file message -> Open Anyway -> open it again.
  • Windows: at "Windows protected your PC", click More info -> Run anyway.

Everyday use

  • In Claude Code / Codex, just ask for data. In Claude Code you can also type `/sancho` and `/sancho-update` directly.

After installation, use a Code session. Sancho is installed

computer-wide, so you do not need to add the sancho-fetch folder in future

sessions. In Claude Desktop, select the Code tab. In **Codex, start a

Code chat**. Regular chats cannot access your local Sancho installation.

  • There are system instructions to make Sancho return non-technical details of each data pull, so if you want these technical details returned, tell your AI:

*"turn on Sancho developer mode."*

  • Something off? Ask your AI to run sancho ready -- it checks your setup and reports what's wrong, without changing your setup. To actually fix problems, ask your AI to run sancho doctor --fix.

Where your files land

your folder/

sancho-downloads/ # <-- files you have downloaded via chatting with AI sancho-workspace/ source/ # managed data modules custom/ # your own modules -- never overwritten when updating Sancho playbooks/ # your repeatable workflows fetched-data/ # faithful archive of every fetch (don't edit - this is a cache so we don't have to use API calls unnecessarily) analysis-data/ # your derived work outputs/ # reports, dashboards, exports logs/ # what Sancho did, when, and why update-backups/ # snapshots before every update .env # your API keys (never logged, never printed)

Every fetch writes a working file you open -- into sancho-downloads/ in the folder that you're working in -- and a faithful archive/cache copy in sancho-workspace/fetched-data/ so repeat requests reuse cached data.

Tables arrive as Excel .xlsx with codes like 01003 kept intact; maps stay GeoJSON/KML; nothing is converted in a way that loses information. Filenames start with the date: 2026-06-08_194211_alabama-population.xlsx.

Add your own data sources

Tell your AI:

Create a Sancho module for [that API / that agency's data / this file I

receive every month].

It's built under custom/, works like the built-in sources, and Sancho updates never overwrite the folder.

A good example of this is, Sancho fetches from Washington DC's and New York City's open data portals, which use a common API/open data portal format. The AI can be asked to create a module for other cities and will intelligently check on its own whether the new city uses the same format or not, and if it does, will reuse the same module code format (and otherwise, create a new one). You'd be surprised at how many cities use the same format so give it a try if you're working in a geographic regional level that is not already covered by Sancho's built-in modules.

Updates

Just ask your AI:

Update Sancho.

The AI upgrades the sancho command from PyPI (uv tool upgrade sancho-fetch) and then migrates your workspace safely, with a backup and a rollback command.

Working from the GitHub repository instead? In GitHub Desktop: Fetch origin -> Pull origin, then tell your AI *"update Sancho."*

When updating, your own files (custom/, playbooks/, fetched-data/, .env, ...) are never touched.

If you want Sancho to stop mentioning new versions in chats (which it will do once every 2 weeks), simply set SANCHO_UPDATE_NUDGE=false in .env to turn it off.

Uninstalling

Same as installing: paste one request to your AI in a Code session:

Uninstall Sancho Fetch from this computer: run sancho uninstall, then

uv tool uninstall sancho-fetch. Keep my sancho-workspace and

sancho-downloads folders -- that is my data and my API keys. Tell me when

it's done and what was removed.

(Those two commands are the whole uninstall if you'd rather run them yourself.)

Sancho only removes what it installed: its skills and its entries in your AI apps. Anything you edited or created yourself is left alone and reported.

Your sancho-workspace/ folder (fetched data, custom modules, .env keys) and your sancho-downloads/ files are always kept. Deleting data is a separate, deliberate step that asks for confirmation first -- ask your AI, or run sancho uninstall --help to see how.

Privacy Policy

Sancho runs on your computer and keeps your data there. Fetches go directly from your machine to the public data provider you chose; API keys are read from your local .env and sent only to that provider. Sancho has no telemetry. Full details: docs/PRIVACY.md.

More documentation

  • CHANGELOG.md -- what changed in each release.
  • README_ALL_INSTRUCTIONS.md -- full operator/AI reference.
  • project-docs/MCP_SERVER_SETUP_CLAUDE_CHATGPT_WEB.md -- MCP setup details for desktop and web clients.
  • project-docs/MODULE_CREATION_GUIDE.md -- how your AI builds new data modules.

Hosting your own version of Sancho

While Sancho is designed to run on your own computer, you can also host it on a server so that users can access it via web rather than needing Claude/Codex desktop apps.

The idea with hosting was primarily to support organizations hosting hands-on workshops for users who may not have the technical skills to install Sancho on their own computers.

  • hosting/README.md -- hosted demo endpoint for events.

For AI assistants

If you are an AI assistant, do not operate from this quick start. Read README_ALL_INSTRUCTIONS.md, then follow CLAUDE.md or AGENTS.md.

License

Code is licensed under MPL 2.0; documentation under CC BY 4.0 unless noted. The Sancho name, logo, and brand assets are protected separately -- see LICENSE, NOTICE, LICENSE-DOCS.md, and INTENDED_USE.md.

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