Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Drop-in MCP proxy. 71% fewer tokens. Session dedup compounds to 92%. Zero code changes.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"gcf-proxy": {
"command": "npx",
"args": [
"-y",
"@blackwell-systems/gcf-proxy"
]
}
}
}在终端中运行这条命令
claude mcp add gcf-proxy -- npx -y @blackwell-systems/gcf-proxy粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"gcf-proxy": {
"command": "npx",
"args": [
"-y",
"@blackwell-systems/gcf-proxy"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"gcf-proxy": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@blackwell-systems/gcf-proxy"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 blackwell-systems 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.10.2)于5个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Spec Driven Development, Remote Desktop Commander, AvaHR
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Read hiring data and create inactive assessment and questionnaire drafts for human review.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
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
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.
Add gcf-proxy in front of any MCP server command:
{
"mcpServers": { "memory": { "command": "gcf-proxy", "args": ["npx", "-y", "@modelcontextprotocol/server-memory"] } } }
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.
--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.
| 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 |
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
@0 fn pkg.Auth 0.78 lsp_resolved ...
53-71% fewer input tokens.
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.
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.
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
More links
MIT - Dayna Blackwell / GCF