MCP-compress-router – MCP Compressor

Hacker News

When you have multiple MCP servers, every request to the LLM will include all of their tools and descriptions, which can quickly eat up your token limit and increase costs. The thing is, most of the time, you don't need all of them. For example, let’s take three popular MCP servers: Notion, GitHub, and Pylance. The overhead they create on every turn is about 26K tokens. If we assume an average 50-turn coding session and Opus pricing, the overhead for a single session is about $0.9275. `mcp-compress-router` does something very simple: it proxies all MCP servers with just two tools: `get_tool_schema` and `invoke_tool`. `invoke_tool` proxies the call to the downstream MCP server. The `get_tool_schema` description lists the tool names and arguments for all downstream MCP server tools so that the agent knows what's available. Whenever it needs a tool, it first calls `get_tool_schema` to read the full description and argument schema, and then calls `invoke_tool`. The savings are pretty serious. The example of 3 MCP servers is compressed to 900 tokens with the "max" compression level (just tool names), or to about 2000 tokens with the "high" compression level (the default one: tool names plus argument names). So you'll be saving 90%+ this way.

Visit product View on Hacker NewsJun 29, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: agent, mcp, single · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: plus, calls · Missing: platform, intuitive, reviews

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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