Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
作者による説明(英語の原文)
Local-first public-data MCP server that writes inspectable records and working files.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
リンク先は作者自身のファイルです。私たちが置いたり再パッケージしたりしたものではありません。ファイルを開くと、Claude Desktopがインストールします。
ターミナルでこれを実行します
claude mcp add sancho-fetch -- uvx sancho-fetch mcp serve --workspace --transport stdioこのサーバーは起動コマンドに値を入れる必要があるので、設定をコピーして入力してください。
プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"sancho-fetch": {
"command": "uvx",
"args": [
"sancho-fetch",
"mcp",
"serve",
"--workspace",
"--transport",
"stdio"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
このサーバーは起動コマンドに値を入れる必要があるので、設定をコピーして入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"sancho-fetch": {
"type": "stdio",
"command": "uvx",
"args": [
"sancho-fetch",
"mcp",
"serve",
"--workspace",
"--transport",
"stdio"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント panth-net で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.3.1)がレジストリに届いたのは2 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにPython (uv), MCPBも必要です。
同じカテゴリには:Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
Agent-Native Google Calendar - manage events, sync, and public booking
Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
Ask your AI for public data. Get real files on your computer.
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
uvisn'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.
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.
Install straight from PyPI.
1. Open your AI's code mode.
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:
.env file in the sancho-workspace folder and paste your key there; orsancho env open). Paste the key into the opened file, **don't paste your keyinto 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:
Cmd+Shift+. (Command + Shift + period).@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.
After installation, use a Code session. Sancho is installed
computer-wide, so you do not need to add the
sancho-fetchfolder in future
sessions. In Claude Desktop, select the Code tab. In **Codex, start a
Code chat**. Regular chats cannot access your local Sancho installation.
*"turn on Sancho developer mode."*
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.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.
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.
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.
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 mysancho-workspaceand
sancho-downloadsfolders -- 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.
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.
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.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.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.
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.