Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Beschreibung vom Entwickler (englisches Original)
Local-first public-data MCP server that writes inspectable records and working files.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Die .mcpb-Datei des Entwicklers herunterladen
Der Link führt zur Datei des Entwicklers selbst; wir hosten sie nicht und verpacken sie nicht neu. Öffne die Datei, dann installiert Claude Desktop sie.
Führe das in deinem Terminal aus
claude mcp add sancho-fetch -- uvx sancho-fetch mcp serve --workspace --transport stdioDieser Server braucht Werte in seiner Befehlszeile, also kopier die Einstellungen und trag sie ein.
In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"sancho-fetch": {
"command": "uvx",
"args": [
"sancho-fetch",
"mcp",
"serve",
"--workspace",
"--transport",
"stdio"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server braucht Werte in seiner Befehlszeile, also kopier die Einstellungen und trag sie ein.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"sancho-fetch": {
"type": "stdio",
"command": "uvx",
"args": [
"sancho-fetch",
"mcp",
"serve",
"--workspace",
"--transport",
"stdio"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto panth-net bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.3.1) kam vor 2 Monaten in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Python (uv), MCPB auf deinem Computer.
In derselben Kategorie: Spec Driven Development, Remote Desktop Commander, OpenTakeoff
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Construction takeoff for AI agents: load plans, set scale, measure, count, export with provenance.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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.