Gcf Proxy MCP server

gcf-proxy

Bidirectional MCP proxy that translates between JSON and GCF. Drop-in, zero changes to your server or client. Works with any structured data format.

100% comprehension on every frontier model. 29% fewer tokens than TOON, 56% fewer than JSON (2,400+ evals, 11 models, 3 providers). Nested object flattening with opt-out for open-weight models. One line change in your MCP config.

Install

pip install gcf-proxy                                         # PyPI
npm install -g @blackwell-systems/gcf-proxy                   # npm
go install github.com/blackwell-systems/gcf-proxy@latest      # Go

Try it (30 seconds, no auth)

gcf-proxy --verbose uvx yfinance-mcp

Use it with any MCP client. When tools return structured JSON, the proxy re-encodes to GCF and logs savings to stderr:

gcf-proxy: get_price_history              54.0KB -> 28.1KB (48% saved)
gcf-proxy: get_ticker_info                10.0KB -> 7.4KB (26% saved)
gcf-proxy: get_price_history              53.8KB -> 27.9KB (48% saved)

--- gcf-proxy session stats ---
Tool calls rewritten:  3
JSON bytes in:         117.8KB
GCF bytes out:         63.4KB
Bytes saved:           54.4KB (46.2%)
Est. tokens saved:     ~13.6K
-------------------------------

Real live stock data from Yahoo Finance. 118KB of JSON reduced to 63KB. ~13,600 tokens saved in 3 tool calls.

Usage

Local server (stdio)

Add gcf-proxy in front of any MCP server command:

{
  "mcpServers": {
    "memory": {
      "command": "gcf-proxy",
      "args": ["npx", "-y", "@modelcontextprotocol/server-memory"]
    }
  }
}

Remote server (HTTP)

Point --upstream at any Streamable HTTP MCP server:

{
  "mcpServers": {
    "remote": {
      "command": "gcf-proxy",
      "args": ["--upstream", "http://host:3000/mcp"]
    }
  }
}

Supports JSON and SSE responses. Session ID tracking via Mcp-Session-Id is automatic.

Deploy as HTTP service

--http turns the proxy into a remote Streamable HTTP server:

gcf-proxy --http :9090 --session your-mcp-server

Any MCP client that supports HTTP transport connects directly. Health check at /health. Chains with --upstream for fully remote deployments.

Both modes are bidirectional: server responses are encoded to GCF, GCF in tool call arguments is decoded to JSON. Neither side needs to change.

Flags

Responses: Server (JSON) -> LLM (GCF)

Shortened. The full README is on GitHub.