GoldenFlow is listed as active in the registry but did not answer our last check. 641 installs a week from pypi. It exposes 3 tools. Last commit 1 May 2026.
Standardize, reshape, and normalize messy data — CSV, Excel, Parquet, S3, databases.
The author archived this repository on GitHub, meaning it is no longer maintained.
We read the source, 23 h ago · tools taken from the live server · rules 3dff92dd89df
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.
этот файл ставится пользователю, но в репозитории его нет
Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.
Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 2 min ago.
claude mcp add goldenflow --transport http https://goldenflow-mcp-production.up.railway.app/mcp/
{
"mcpServers": {
"goldenflow": {
"url": "https://goldenflow-mcp-production.up.railway.app/mcp/"
}
}
}
[mcp_servers.goldenflow]
url = "https://goldenflow-mcp-production.up.railway.app/mcp/"
{
"mcpServers": {
"goldenflow": {
"url": "https://goldenflow-mcp-production.up.railway.app/mcp/"
}
}
}
{
"mcpServers": {
"goldenflow": {
"url": "https://goldenflow-mcp-production.up.railway.app/mcp/"
}
}
}
Read directly from the server with tools/list, grouped by what they act on.
If a tool disappears, we record the date.
add
echo
server_time
| URL | Transport | State | Latency | Checked |
|---|---|---|---|---|
| https://goldenflow-mcp-production.up.railway.app/mcp/ | streamable-http | answering | 344 ms | 2 min ago |
Read-only WHOOP v2 data for MCP clients, with local SQLite cache and CSV/JSONL/Parquet exports.
Create interactive visualizations and query data sources (SQLite, CSV, Parquet, JSON)
MCP server for FastBCP — high-performance parallel database export to files and cloud
MCP server for LakeXpress — automated database-to-cloud data pipeline as Parquet
Markdown memory for AI agents. Files you can read, edit, grep, and commit. Not a database.
Tabular data for AI agents — load CSV/JSON/XLSX, query, group-by, stats (no database).
Notion for AI agents — search, read pages/blocks, query databases, create pages.
Create, update, and manage pages, databases, and workspace users
Answers built from our own checks of this server.