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PopNx MCPMCP servers for people who use AI · explained in plain words

apifacts

by Illouminusversion 0.2.1i★ –iRuns on your computer
Registry nameio.github.Illouminus/apifacts

Install

Overview

Description from the maker (original English)

Verified facts and typed fixes for failing third-party API calls (Stripe, GitHub, Google…)

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Online shops & payments

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "apifacts": {
      "command": "uvx",
      "args": [
        "apifacts",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add apifacts -- uvx apifacts mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "apifacts": {
      "command": "uvx",
      "args": [
        "apifacts",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "apifacts": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "apifacts",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account Illouminus, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Online shops & paymentsi

  • More stars than 0% of the other 1751 servers in this category
  • Published to the registry more recently than 72% of the other 1751 servers in this category
  • Basic checks equal to or better than 100% of the other 1751 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is apifacts safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.2.1) reached the registry 12 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spree Commerce: open-source B2B and marketplace ecommerce, Stripe MCP, MCP Cn Commerce

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Stripe MCP

by stripe

Stripe’s official server lets AI view and manage customers, products, payments and invoices. Connect to Stripe’s cloud and sign in with OAuth.

Runs in the cloud★ 2,043MITUpdated 12 months ago
Install

MCP Cn Commerce

by TonyWang-hub

8 read-only MCP platform entries; support is operation-specific; merchant live acceptance pending

Runs on your computer★ 96MITUpdated 28 days ago
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

apifacts

A verified changelog of reality for AI agents. An open registry of typed, dated facts about what third-party machine interfaces (HTTP APIs, SDK packages, CLIs) actually do today, as opposed to what their docs say.

Why

Interfaces change, docs lag, and nobody tells the agent. Every session pays tokens to rediscover the same traps: a 200 with an empty array instead of a 429, a rate limit reported as status: 0 in the body, an SDK release that silently dropped a field. Documentation tools answer "give me the right docs"; apifacts answers "the docs are wrong about this endpoint right now, here is the verified difference".

Install

pip install apifacts-pytest   # pytest plugin: a test failing on an HTTP error prints the known facts

uvx apifacts check https://api.stripe.com/v1/charges -X GET --status 401 # CLI (and library) npx skills add Illouminus/apifacts # agent skill: on an API error, ask the registry first

Both packages are on PyPI, carry the same version and ship together from one tag (release). The plugin sends nothing until a test fails. The skill (skills/apifacts/SKILL.md) tells Claude Code and other agents when to call apifacts check and how to read the answer.

CI

Agents that fix a red build read the failing step's log, the annotations and the job summary. The GitHub Action puts the known facts in all three (action/README.md):

- uses: Illouminus/apifacts/action@v0

with: run: pytest -q # same exit code as without the action; scanned only when it fails

Anywhere else, pipe a log through apifacts scan (uvx apifacts scan build.log): it finds the failed HTTP calls that requests, httpx, gh, axios and curl -f print, and asks about each.

MCP

One tool, check(target, method, status, body), over stdio; no resources, no prompts. Claude Code:

claude mcp add apifacts -- uvx --from 'apifacts[mcp]' apifacts mcp

Claude Desktop, Cursor and other clients (mcpServers in their JSON config):

{"mcpServers": {"apifacts": {"command": "uvx", "args": ["--from", "apifacts[mcp]", "apifacts", "mcp"]}}}

apifacts mcp --offline answers from the snapshot packaged in the wheel and sends nothing.

How a fact is verified

  • One fact per YAML file in facts/ /, with a kind (error_meaning, rate_limit, breaking_change, ...), the exact error signal, a one-sentence claim, dimensions (env, plan, region, account_state) and evidence.
  • Truth comes only from deterministic probes: declarative HTTP steps and assertions that our runner executes on a schedule. No language model is called anywhere in the verification path.
  • Fixes are data, not advice: members of a closed vocabulary with typed parameters (no free text, no shell, no foreign URLs), rendered to one English sentence by us.
  • A probe in another environment never refutes a fact; it adds a dimension.

Calling check()

One endpoint answers and learns at the same time: the request is the contribution. Send the target and, optionally, the error you got; receive matching facts with typed fixes and what the probes observe. If the target matches a registered template, a normalized observation is stored (no bodies, headers, query strings or raw IPs).

curl -s https://apifacts.dev/v0/check \

-H 'Content-Type: application/json' \ -d '{"target": {"method": "POST", "url": "https://api.stripe.com/v1/payment_intents"}, "error": {"status": 400, "body_excerpt": "Keys for idempotent requests can only be used with the same parameters they were first used with."}}'

The response is never empty: facts, observed, and an unknown.reason when nothing matches.

Status

The API is live at https://apifacts.dev (/v0/check, /v0/services, /openapi.json, /healthz; facts.baillot.dev, the old address, redirects there). The registry covers the services and facts listed at apifacts.dev/registry, each fact page showing how it was established; /llms.txt describes the API for agents. Design contracts live in docs/specs/.

Running your own copy: the API reads facts/ and services/ once, at startup, so after a git pull restart it; ops/deploy.sh checks out, syncs and restarts in one step.

Contributing

Calling check() is contributing. Pull requests are for humans and API producers: see CONTRIBUTING.md. Producer signatures are always free.

License

Code: MIT. Data (facts/ and the generated fact pages): CC BY 4.0.

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