jmunch-mcp
OfficialWraps the Brave Search MCP server, reducing token cost of search results by handle-ifying responses and allowing agents to query them with jmunch verbs.
Provides a gateway that integrates with CrewAI applications, saving tokens by handle-ifying large tool results and injecting jmunch verbs.
Wraps the GitHub MCP server, forwarding calls and handle-ifying large tabular responses (issues, PRs, commits) into a SQLite backend for efficient querying via peek, slice, and aggregate verbs, reducing token usage by up to 88%.
Provides a gateway that integrates with LangChain applications, saving tokens by handle-ifying large tool results and injecting jmunch verbs.
Provides a gateway that integrates with Ollama-compatible applications, saving tokens by handle-ifying large tool results and injecting jmunch verbs.
Provides a gateway that proxies the OpenAI API, transparently handle-ifying large tool results and injecting jmunch verbs to reduce token usage for OpenAI SDK applications.
Wraps the Slack MCP server, reducing token cost of large Slack responses by handle-ifying them for efficient querying.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@jmunch-mcpproxy the github mcp server and peek at my recent pull requests"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 ( |
| 379,878 | 44,328 | 335,550 (88.3%) |
Firecrawl (Wikipedia scrapes + site map + search) |
| 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.
Related MCP server: Bifrost-MCP Gateway
Install
pip install jmunch-mcpFrom source:
git clone https://github.com/jgravelle/jmunch-mcp
cd jmunch-mcp
pip install -e .Quickstart
jmunch-mcp initinit 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 <name>.toml per selection into ./configs/. Non-interactive flags: --yes (pick everything already registered in a client), --dry-run, --overwrite, --out <dir>, --no-running, --no-catalog.
Manual
jmunch-mcp --config examples/config.tomlConfigure your MCP client to launch jmunch-mcp --config <path> 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]'
jmunch-mcp gateway --config configs/gateway.example.toml
# listening on http://127.0.0.1:7879Point your app:
# OpenAI SDK, LangChain, Aider, Continue, Cline, Ollama-compat apps:
export OPENAI_API_BASE=http://127.0.0.1:7879/v1
# Native Anthropic SDK / Claude Code:
export ANTHROPIC_BASE_URL=http://127.0.0.1:7879What it does, transparently:
Handle-ifies fat tool_results in outgoing requests — your app's tool returns 100KB of JSON, the model sees a 1KB summary + opaque handle.
Injects jmunch verbs (
peek,slice,search,aggregate,describe,summarize,list_handles) into the request'stoolsarray so the model can drill in.Short-circuits verb calls — when the model calls
jmunch_peek, the gateway resolves it locally against the handle registry and synthesizes the follow-up turn. The app never sees jmunch tool_calls; those completions cost zero upstream tokens.Persists handles to
~/.jmunch/handles.dbwith a configurable TTL so they survive restarts and cross-session reads.Streams both ways — OpenAI SSE and Anthropic event streams are buffer-then-replayed with correct verb resolution.
Per-request controls via headers:
X-Jmunch-Upstream: <name>— override the configured upstream.X-Jmunch-Inject: false— disable tool injection for this call (pure pass-through + request-side handle-ify only).
Metrics flow into the same dashboard as the MCP proxy. Filter with ?surface=gateway or ?surface=mcp on /api/stats and /api/calls.
Dashboard
A read-only local web UI over the metrics DB each proxy writes to. Shows cumulative totals, per-upstream breakdowns, and a time series of forwarded calls.
jmunch-mcp dashboard # http://127.0.0.1:7878
jmunch-mcp dashboard --open # also open in your default browserFlags: --port (default 7878), --host (default 127.0.0.1), --db <path> to point at a non-default metrics DB, --open to launch the browser. Metrics only populate once proxies have recorded calls, so run your client against a wrapped upstream first.
License
jmunch-mcp is released under the MIT License — free to use, modify, distribute, and embed in commercial products.
Note that licensing of upstream MCP services you proxy through jmunch-mcp is governed by those services' own terms. This applies to third-party MCP servers (GitHub, filesystem providers, vendor APIs) and to any sibling tools in the broader retrieval ecosystem you may compose with — check each upstream's license before redistribution.
This server cannot be deployed
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