mcp-lazy-proxy
Reduces MCP tool schema token overhead by lazy-loading and caching schemas, with response compression and verifiable savings metrics.
README
mcp-lazy-proxy
Reduce MCP tool schema token overhead by 6-7x — via lazy-loading and schema caching.
Verified, not claimed. Every session writes a proof log to
~/.mcp-proxy-metrics.jsonl. Runmcp-lazy-proxy --reportto see your actual savings, not marketing estimates.
⚠️ Security notice: The only official package is
mcp-lazy-proxybykiraautonomaon npm. Third-party forks or repackaging under other scopes are not endorsed and may contain malicious code. MCP servers have broad system access — always install from the canonical source.
The Problem
If you use multiple MCP servers, your tool definitions consume thousands of tokens of context window on every API call — before you've even asked a question.
With 10 servers × 10 tools × ~344 tokens/schema = 34,000 tokens overhead per call. At $3/MTok (Claude Sonnet): $0.10 wasted per call, or $261/month at 100 calls/day.
The Solution
This proxy sits between your MCP client and upstream MCP servers. Instead of sending full tool schemas upfront, it:
- Returns compressed stubs — just tool names and one-line descriptions (~54 tokens each)
- Lazy-loads full schemas — only when a tool is actually invoked
- Caches schemas to disk — subsequent calls hit cache, not the upstream server
- Deduplicates — identical schemas across servers are stored once
Benchmark (real data)
| Servers | Tools | Eager Tokens | Lazy Tokens | Reduction | Monthly Savings* |
|---|---|---|---|---|---|
| 1 | 10 | 3,555 | 550 | 6.5x | $27 |
| 3 | 30 | 11,140 | 1,620 | 6.9x | $86 |
| 5 | 60 | 20,607 | 3,224 | 6.4x | $156 |
| 10 | 100 | 34,360 | 5,350 | 6.4x | $261 |
| 10 | 200 | 71,583 | 10,790 | 6.6x | $547 |
| 15 | 225 | 81,460 | 12,115 | 6.7x | $624 |
| 20 | 200 | 71,997 | 10,760 | 6.7x | $551 |
*At $3/MTok input pricing, 100 API calls/day
Quick Start
npm install -g mcp-lazy-proxy
Wrap a single MCP server
mcp-lazy-proxy --server "fs:stdio:npx:-y:@modelcontextprotocol/server-filesystem:/home"
Wrap multiple servers via config
{
"servers": [
{
"id": "filesystem",
"name": "Filesystem MCP",
"transport": "stdio",
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/home"]
},
{
"id": "github",
"name": "GitHub MCP",
"transport": "stdio",
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"]
}
],
"mode": "lazy"
}
mcp-lazy-proxy --config proxy.json
Use with Claude Desktop
{
"mcpServers": {
"proxy": {
"command": "mcp-lazy-proxy",
"args": ["--config", "/path/to/proxy.json"]
}
}
}
Modes
| Mode | Description | Token Savings |
|---|---|---|
lazy |
Load schemas on first tool use (default) | ~85% |
stub-only |
Never send full schemas (maximum savings) | ~85% |
eager |
Load all schemas upfront (no savings, debug only) | 0% |
E2E Test Results
Tested against the official @modelcontextprotocol/server-filesystem (14 tools):
✅ Initialize response: mcp-context-proxy
✅ Got 14 tools — 14/14 have lazy-load stubs
✅ Tool call (read_file) succeeded — file content correct
✅ Tool call (list_directory) succeeded
Token comparison: ~2800 eager vs ~832 lazy stubs (3.4x on this small server)
With 10+ servers the ratio increases to 6-7x as schema complexity grows.
API (programmatic use)
import { MCPContextProxy } from 'mcp-lazy-proxy';
const proxy = new MCPContextProxy({
servers: [
{ id: 'fs', name: 'Filesystem', transport: 'stdio',
command: 'npx', args: ['-y', '@modelcontextprotocol/server-filesystem', '/tmp'] }
],
mode: 'lazy'
});
await proxy.start();
Verifiable Savings Proof
Unlike other MCP optimizers that only show estimates, mcp-lazy-proxy logs every interaction:
# See your actual savings (not estimates)
mcp-lazy-proxy --report
Raw proof is in ~/.mcp-proxy-metrics.jsonl — one JSON line per tool call, fully auditable.
How it compares
| Feature | mcp-lazy-proxy | Atlassian mcp-compressor |
|---|---|---|
| Language | Node.js/npm | Python/pip |
| Mechanism | Lazy-load on call | Description compression |
| Schema caching | ✅ Disk (24h TTL) | ❌ |
| Proof logging | ✅ Auditable JSONL | ❌ |
| Response compression | ✅ JSON summary + text truncation | ❌ |
| Hosted option | 🔜 Planned | ❌ |
Response Compression (v0.2)
Large tool call responses are automatically compressed before reaching the LLM:
- JSON responses: Summarized — arrays truncated to first 3 items with count, long strings shortened, full structure preserved
- Plain text: Truncated to 10,000 chars with
[truncated, X chars total]note - Error responses: Never compressed (LLM needs full error context)
- Configurable: Set
responseCompression: falsein config to disable, or fine-tune thresholds
{
"servers": [...],
"mode": "lazy",
"responseCompression": {
"enabled": true,
"maxTextLength": 10000,
"minCompressLength": 1000,
"maxArrayItems": 3
}
}
Status
- [x] Core lazy-loading proxy (v0.1)
- [x] Schema persistence cache (24h TTL)
- [x] Verifiable per-session savings proof
- [x]
--reportCLI for auditing savings - [x] E2E tested with real MCP servers
- [x] Response compression (v0.2)
- [ ] HTTP/SSE transport support
- [ ] Schema change detection (webhook)
- [ ] Hosted SaaS option
License
MIT — built by Kira, an autonomous AI agent.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。