Gcf Proxy

Name: Gcf Proxy
Author: blackwell-systems

by blackwell-systemsversion 0.10.2i★ –iRuns on your computer

Registry nameio.github.blackwell-systems/gcf-proxy

Install

Overview

Description from the maker (original English)

Drop-in MCP proxy. 71% fewer tokens. Session dedup compounds to 92%. Zero code changes.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "gcf-proxy": {
      "command": "npx",
      "args": [
        "-y",
        "@blackwell-systems/gcf-proxy"
      ]
    }
  }
}

Run this in your terminal

claude mcp add gcf-proxy -- npx -y @blackwell-systems/gcf-proxy

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "gcf-proxy": {
      "command": "npx",
      "args": [
        "-y",
        "@blackwell-systems/gcf-proxy"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "gcf-proxy": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@blackwell-systems/gcf-proxy"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account blackwell-systems, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32568 servers in this category
Published to the registry more recently than 13% of the other 32568 servers in this category
Basic checks equal to or better than 100% of the other 32568 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Gcf Proxy safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.10.2) reached the registry 5 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, FlyDocs

Alternatives

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Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Flag	Description
`--session`	Enable session dedup (bare refs for previously-transmitted symbols)
`--cache`	Cache encoded responses for identical tool calls
`--delta`	Send only changed symbols when a tool's response changes slightly
`--no-flatten`	Use expanded encoding for nested objects (open-weight models currently comprehend this form better; GCF still outperforms JSON either way)
`--min-size N`	Skip encoding for responses smaller than N bytes (default: 100)
`--stream-threshold N`	Min symbols before streaming mode activates (default: 5)
`--stats-file PATH`	Write JSON stats to file after each call
`--upstream URL`	Connect to a remote MCP server over HTTP
`--http ADDR`	Serve MCP over Streamable HTTP
`--no-progress`	Disable progress notifications
`--verbose`	Log per-call savings to stderr

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

Before: {"tool":"context_for_task","symbols":[{"qualified_name":"pkg.Auth","kind":"function","score":0.78,...},...]}

After: GCF profile=graph tool=context_for_task budget=5000 tokens=1900 symbols=50 edges=20

targets

@0 fn pkg.Auth 0.78 lsp_resolved ...

53-71% fewer input tokens.

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

If the LLM produces GCF in a tool call argument (63% fewer output tokens), the proxy decodes it to JSON before forwarding:

LLM sends:    {"tool": "process", "arguments": {"data": "GCF profile=generic\nname=Alice\nage=30\n"}}

Server gets: {"tool": "process", "arguments": {"data": {"name": "Alice", "age": 30}}}

Detection is a 4-byte prefix check (GCF ). Zero overhead. Non-GCF strings pass through untouched.

How it works

Spawns your MCP server as a subprocess
Proxies stdin/stdout between client and server
Responses: intercepts JSON-RPC responses, re-encodes structured JSON as GCF
Requests: scans tool call arguments for GCF strings, decodes to JSON
Passes everything else through unchanged in both directions

Why not modify the server?

Sometimes you can't. The server is a third-party binary, or it's maintained by another team, or you just don't want to add a dependency. gcf-proxy gives you the token savings without touching server code.

If you control the server, use the GCF libraries directly for better control over session deduplication and delta encoding.

Benchmarks

100% general comprehension on every frontier model. 91.2% on adversarial code graphs (vs TOON 68.8%, JSON 54.1%). Wins 15/16 datasets on token benchmark.

Eval	GCF	TOON	JSON
General comprehension	100%	100%	100%
Adversarial code graphs (500 symbols)	91.2%	68.8%	54.1%
Token efficiency (16 datasets)	15/16 wins	1/16	baseline

Reproduce comprehension eval: git clone https://github.com/blackwell-systems/gcf-go && cd gcf-go/eval && GOWORK=off go test -run TestComprehension -v -timeout 0

Reproduce token benchmark: git clone https://github.com/blackwell-systems/toon && cd toon && git checkout gcf-comparison && cd benchmarks && pnpm install && pnpm benchmark:tokens

License

MIT - Dayna Blackwell / GCF

Gcf Proxy

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

FlyDocs

README from the maker

gcf-proxy

Install

Try it (30 seconds, no auth)

Usage

Local server (stdio)

Remote server (HTTP)

Deploy as HTTP service

Flags

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

targets

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

How it works

Why not modify the server?

Benchmarks

Links

License