gcf-proxy MCP Server | MCP Server List

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.

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

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.

Addgcf-proxyin front of any MCP server command:

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

Point--upstreamat any Streamable HTTP MCP server:

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

Supports JSON and SSE responses. Session ID tracking viaMcp-Session-Idis automatic.

--httpturns 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--upstreamfor 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.

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 ...

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.
- 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

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 theGCF librariesdirectly for better control over session deduplication and delta encoding.

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.

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

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- Documentation
- Cost Calculator
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gcf-proxy

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

About

Details

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Setup

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

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