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fxray MCP Server

local only

fxray runs on your own machine — the client starts it, so there is no endpoint to ping. 62 installs a week from pypi.

Power Apps via pac: solutions, canvas Power Fx source, Dataverse metadata, FetchXML, gated imports.

The linked repository no longer exists on GitHub — it was deleted or made private.

Installs per day peak 15 · avg 8 · +38% w/w
a month agotoday
62
Installs / week
pypi · fxray
—
Stars
on GitHub
—
Last commit
from GitHub
—
License
language unknown

What the code does

We have not read this server's code yet, 31 d ago · rules ccca72095570

Evidence

Things with no honest explanation: a promise that contradicts the code, code that runs at install time while hiding what it does, data leaving the machine.

Name looks like a popular package fray
fxray похож на fray
Capabilities

What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.

File ships in the package but is absent from the source [пакет] src/fxray/__init__.py:1, [пакет] src/fxray/__main__.py:1, [пакет] src/fxray/canvas.py:1 и ещё 12
этот файл ставится пользователю, но в репозитории его нет
Unsafe deserialization [пакет] src/fxray/canvas.py:83
                    st = yaml.load(text, Loader=_Loader) or {}
Runs an external command [пакет] src/fxray/pac.py:58
        return subprocess.Popen([self.pac_path, *args], stdin=subprocess.DEVNULL, stdout=subprocess.PIPE,
Dumps the whole environment [пакет] src/fxray/pac.py:57
        env = dict(os.environ)

Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.

We found things in this code

Code changes quietly between releases, and nobody reads the diff of a dependency. We do, on every release — watch fxray and you get told the day something new turns up.

Three servers free · no card

Connect this server

This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.

run in your terminal
claude mcp add fxray -- uvx fxray
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "fxray": {
      "command": "uvx",
      "args": [
        "fxray"
      ]
    }
  }
}
~/.codex/config.toml
[mcp_servers.fxray]
command = "uvx"
args = ["fxray"]
.cursor/mcp.json
{
  "mcpServers": {
    "fxray": {
      "command": "uvx",
      "args": [
        "fxray"
      ]
    }
  }
}
.vscode/mcp.json
{
  "servers": {
    "fxray": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "fxray"
      ]
    }
  }
}

Optional settings from the registry entry, left out of the block above: FXRAY_CONFIG (Path to fxray.toml (environment allowlist, write gate, pac path). Optional if the file is in the working folder or the per-user folder), FXRAY_WORKSPACE (Folder whose fxray.toml to use when FXRAY_CONFIG is not set), FXRAY_HOME (Per-user folder (default %LOCALAPPDATA%\fxray on Windows, ~/.config/fxray elsewhere)).

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fxray — questions

Answers built from our own checks of this server.

Why is there no uptime for fxray?
fxray runs on your own machine over stdio — there is no network address to reach, so uptime cannot be measured for it by anyone. What can be measured is adoption: the pypi package fxray was installed 62 times last week.
Is fxray still maintained?
The linked repository no longer exists on GitHub — it was deleted or made private. We show this because it changes what you can expect: an unmaintained server may keep answering for months and then stop without warning.
How do I connect fxray?
Copy the ready config from this page. We generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each in the form that client actually reads. It runs locally, so the command pulls fxray straight from pypi; nothing to host, nothing to sign up for.
How many people use fxray?
The pypi package fxray was installed 62 times in the last week. Week over week that is +38%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.