
jgravelle/jmunch-mcp 是从 https://github.com/jgravelle/jmunch-mcp. 导入的 MCP 服务器候选
概览
jgravelle/jmunch-mcp 是从 https://github.com/jgravelle/jmunch-mcp. 导入的 MCP 服务器候选
概述(AI 翻译)
透明的MCP代理,降低几乎所有其他MCP服务器大型上游工具响应的令牌成本。封装单个上游MCP,转发所有调用,并将肥厚的负载处理成代理可用少量通用动词(peek、slice、search、aggregate、describe、list_handles)查询的内容感知后端。
基准测试
端到端对两个流行的现实MCP服务器进行测量。每次运行都会触发固定的工具调用脚本两次——一次直接,一次通过jmunch-mcp——并在代理端后进三次jmunch.*动词调用,以模拟代理钻探大结果而非整体吞噬。
| 套件 | 上游 | 直接令牌 | 通过JMUNCH-MCP | 已保存 |
|---|---|---|---|---|
GitHub (facebook/react 问题/PR/提交) |
@modelcontextprotocol/server-github |
379,878 | 44,328 | 335,550(88.3%) |
| 火搜(维基百科抓取 + 网站地图 + 搜索) | firecrawl-mcp |
259,574 | 2,928 | 256,646(98.9%) |
尽管有额外的动词调用,代理在两个套件上墙上的时间也更快——客服从不需要翻阅大量负载:
|套房 |直达 |经JMUNCH-MCP |达美 |
本页由 AI 翻译,技术细节请以原文为准。
README
以下为原文 README 节选,完整内容请查看原文。
jmunch-mcp
Transparent MCP proxy that reduces the token cost of large upstream tool responses for nearly every other MCP server imaginable. Wraps a single upstream MCP, forwards every call, and handle-ifies fat payloads into content-aware backends the agent can query with a small set of universal verbs (peek, slice, search, aggregate, describe, list_handles).
Benchmarks
Measured end-to-end against two popular real-world MCP servers. Each run fires a fixed script of tool calls twice — once direct, once through jmunch-mcp — with three follow-up jmunch.* verb calls on the proxied side to model an agent drilling into a large result rather than slurping it whole.
| suite | upstream | direct tokens | via jmunch-mcp | saved |
|---|---|---|---|---|
GitHub (facebook/react issues/PRs/commits) |
@modelcontextprotocol/server-github |
379,878 | 44,328 | 335,550 (88.3%) |
| Firecrawl (Wikipedia scrapes + site map + search) | firecrawl-mcp |
259,574 | 2,928 | 256,646 (98.9%) |
Wall-clock time was also faster with the proxy on both suites, despite the extra verb calls — the agent never has to page through the fat payload:
| suite | direct | via jmunch-mcp | delta |
|---|---|---|---|
| GitHub | 8.4s | 6.8s | −1.6s (−19.0%) |
| Firecrawl | 16.4s | 9.2s | −7.2s (−43.9%) |
Tabular content (GitHub) routes to the SQLite backend and answers peek/slice/aggregate; JSON content (Firecrawl scrape/map) routes to the JSON-tree backend and answers peek/slice (JSONPath)/search. See bench/README.md to reproduce.
Install
pip install jmunch-mcp
From source:
git clone https://github.com/jgravelle/jmunch-mcp
cd jmunch-mcp
pip install -e .
Quickstart
jmunch-mcp init
init scans three sources — your MCP client configs (Claude Desktop, Claude Code, Cursor, Windsurf, Continue), running processes, and a small catalog of popular upstreams (GitHub, Firecrawl, filesystem, fetch, Brave Search, Slack) — and renders a checklist. Tick the upstreams you want wrapped, and it writes one .toml per selection into ./configs/. Non-interactive flags: --yes (pick everything already registered in a client), --dry-run, --overwrite, --out , --no-running, --no-catalog.
Manual
jmunch-mcp --config examples/config.toml
Configure your MCP client to launch jmunch-mcp --config instead of the upstream server directly. Add --report to print a session summary on shutdown.
Gateway mode (v2 — universal proxy)
The MCP proxy above saves tokens for MCP clients. The gateway saves tokens for any AI application that speaks the OpenAI or Anthropic HTTP API — LangChain, LlamaIndex, CrewAI, AutoGen, Continue, Cline, Aider, or a raw SDK. No code changes in the app; just point base_url at jmunch.
pip install 'jmunch-mcp[gateway]'
安装
This server does not publish a one-line install command.
Open the repository installation guide 新手?先看 mcpskill.org 教程 →