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MCP Flowcore Platform

by flowcore

Connects AI assistants to Flowcore for authenticated data ingestion, analysis, cross-referencing, and platform management.

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Install MCP Flowcore Platform

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "flowcore-mcp-flowcore-platform": {
      "command": "npx",
      "args": [
        "-y",
        "@flowcore/platform-mcp-server"
      ],
      "env": {
        "USERNAME": "YOUR_VALUE_HERE",
        "PAT": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

Install not yet sandbox-tested— we haven't run this command through our automated sandbox yet. Not a red flag; we're working through the catalog.

Overview

flowcore/mcp-flowcore-platform MCP server provides an MCP interface to the Flowcore Platform. It lets AI assistants perform platform actions, ingest data, and analyse or cross-reference information in private or public data cores through natural-language requests. The package runs locally through Node.js tooling and authenticates with a Flowcore username and personal access token. Reach for it when an agent needs to work with Flowcore resources rather than a general-purpose database or search index.

Use cases

  • Ingest operational data into Flowcore through an AI assistant
  • Analyse records stored in Flowcore data cores
  • Cross-reference information across private and public data cores
  • Manage Flowcore resources with natural-language requests

Key features

  • MCP interface for the Flowcore Platform
  • Data ingestion
  • Data analysis and cross-referencing
  • Access to private and public data cores
  • Username and PAT authentication

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 3d ago

Recent health check succeeded.

18/18 checks healthy over the last 79d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 9d ago via OSV.dev · @flowcore/platform-mcp-server (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars9Stargazers on the source repository.
npm downloads1.3kPackage downloads in the last 30 days.
Last commit1y agoMost recent push to the default branch.
Directory activity6 viewsConfig copies, upvotes, and views on AllMCPs.

How MCP Flowcore Platform works

What flowcore/mcp-flowcore-platform MCP server does

The flowcore/mcp-flowcore-platform MCP server gives MCP-compatible AI assistants a structured way to interact with the Flowcore Platform. Its stated scope includes managing and using Flowcore resources, ingesting data, analysing data, and cross-referencing information held in data cores. The available information also says that agents can work with both the user's data cores and public data cores.

Requests are intended to be expressed in human language. MCP provides the interface between the assistant and the platform, while Flowcore remains the system where the relevant data and resources are managed.

How it works

The package starts an MCP server locally and exposes a standardized interface for Flowcore operations. An assistant sends an interpreted request through MCP; the server then uses the supplied Flowcore credentials to access the platform and return the result to the client.

The project supports several launch paths: direct execution with npx, a globally installed npm package, the development source through Bun, or the compiled JavaScript output through Node. The documented package name is @flowcore/platform-mcp-server.

Setup and configuration

The documented direct-run command is:

Terminal
npx -y @flowcore/platform-mcp-server --username <username> --pat <pat>

Replace the placeholders with a Flowcore username and PAT when configuring the command. The README also identifies USERNAME and PAT as required environment variables, representing the same two credentials. A PAT is a Flowcore Personal Access Token and should be handled as a secret.

For a global installation, install @flowcore/platform-mcp-server with npm and start it using the platform-mcp-server executable with the username and PAT options. Developers can instead use Bun to install dependencies and run src/index.ts, or build the project and execute dist/cli.js with Node.

Tools and capabilities

  • Perform actions in the Flowcore Platform.
  • Ingest data into Flowcore.
  • Analyse information stored in data cores.
  • Cross-reference data across available cores.
  • Use information from private or public data cores.
  • Manage and interact with Flowcore resources through MCP.

The material does not specify whether each capability is exposed as a separate MCP tool, resource, or prompt, nor does it document access controls for individual data cores.

Limitations and notes

The flowcore/mcp-flowcore-platform MCP server requires Flowcore credentials; it is not described as an anonymous connector. Validate those details before deploying it in a production agent.

The README points to a separate Flowcore local read-model MCP server for workloads that need reduced hallucinations, lower token use, larger query coverage, or faster querying. That is a different project and is not part of this server's installation or capability set. The repository excerpt also does not state a software license or pricing policy.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about MCP Flowcore Platform

Run the published npm package with npx: `npx -y @flowcore/platform-mcp-server --username <username> --pat <pat>`, replacing the placeholders with your Flowcore credentials.

It requires a Flowcore username and PAT, or Personal Access Token.

It provides an MCP interface for managing and interacting with Flowcore, including actions, data ingestion, analysis, and cross-referencing across private or public data cores.

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