About mcp-computeflux
mcp-computeflux is a lightweight stdio MCP server for calling models through ComputeFlux or another OpenAI-compatible endpoint. It suits MCP hosts that need model discovery and chat completions as tools.
Key features
- 01List endpoint model IDs
- 02Run non-streaming chat completions
- 03Configure an OpenAI-compatible base URL
- 04Run without installing server dependencies
Use cases
- 01Discover models available from an inference endpoint
- 02Call a model from an MCP host
- 03Use a different OpenAI-compatible API endpoint
MCP support
Exposes model listing and chat completion tools to MCP clients over newline-delimited JSON-RPC on stdio. An API key is required for the endpoint.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEmcp-computeflux
Zero-dependency MCP (Model Context Protocol) server. Give any MCP host — Cursor, Claude Desktop, Windsurf, any MCP client — one OpenAI-compatible endpoint, and call models on ComputeFlux as plain tools: list what's available, then run a chat completion.
Your MCP agents hit flash-tier models over one OpenAI-compatible endpoint.
- ~175 lines of Node 18+, no
npm installto run the server (only the optionalclient/e2e harness pulls SDK deps). - Backend-agnostic: point it at any OpenAI-compatible API — ComputeFlux is the default, not the only target.
- ComputeFlux is an OpenAI-compatible, TEE-verifiable multi-model inference gateway deployed on the Polkadot testnet.
Tools
| Tool | What it does |
|---|---|
computeflux_models | Lists the model ids served by the endpoint. |
computeflux_chat | Runs a non-streaming chat completion (model + OpenAI messages; optional max_tokens, temperature). Returns model, finish reason, latency, usage, and the assistant text. |
Configuration (env)
| Variable | Required | Default | Notes |
|---|---|---|---|
COMPUTEFLUX_API_KEY | yes | — | Bearer key for the endpoint. |
COMPUTEFLUX_BASE_URL | no | https://api.computeflux.ai/v1 | Any OpenAI-compatible base URL. |
export COMPUTEFLUX_API_KEY="your_c***_key"
# export COMPUTEFLUX_BASE_URL="https://your-openai-compat-host/v1" # optional
Install
Published on the official MCP Registry (.mcpb bundle, no npm step):
io.github.computeflux2026isgod/mcp-computeflux — listed on the official MCP Registry (search by this name).
Any MCP client / agent that supports registry install (mcp add, Cursor, Claude Desktop,
Claude Code) can pull it by name. Direct git-clone quickstart:
git clone https://github.com/computeflux2026isgod/mcp-computeflux
export COMPUTEFLUX_API_KEY="your_...key"
node /path/to/mcp-computeflux/server.mjs # stdio JSON-RPC
mcpServers snippet:
{
"mcpServers": {
"computeflux": {
"command": "node",
"args": ["/path/to/mcp-computeflux/server.mjs"],
"env": { "COMPUTEFLUX_API_KEY": "your_c***_key" }
}
}
}
Local e2e (optional)
client/ contains a tiny harness using the official MCP SDK — the exact path
Cursor / Claude Desktop use. It spawns the server over stdio, lists tools, and runs
one chat completion with a unique-token round-trip fidelity check.
cd client && npm install && npm run e2e # expects COMPUTEFLUX_API_KEY in the .env it points to
Transport
Newline-delimited JSON-RPC 2.0 over stdio. Only protocol messages are written to stdout; logs go to stderr.
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.