mcp-slim-guard
Lightweight MCP security proxy — compression (up to 86%) + SSRF protection + allow/deny + audit + rate limiting + injection detection
README
type: Readme title: mcp-slim-guard timestamp: "2026-07-23T18:00:00+08:00" description: Lightweight MCP security proxy — compression (up to 86%) + SSRF protection + allow/deny + audit + rate limiting + injection detection tags:
- mcp-slim-guard
- readme
- mcp
- security
- compression
<p align="center"> <a href="./README_CN.md">中文文档</a> · <strong>English</strong> </p>
<h1 align="center">🛡️ mcp-slim-guard</h1>
<p align="center"> <b>One proxy. Two superpowers: compression + security.</b> </p>
<p align="center"> <a href="https://www.npmjs.com/package/mcp-slim-guard"><img src="https://img.shields.io/npm/v/mcp-slim-guard" alt="npm version"></a> <img src="https://img.shields.io/badge/node-%3E%3D18-brightgreen" alt="Node >=18"> <img src="https://img.shields.io/badge/tests-402%20passed-green" alt="402 tests"> <img src="https://img.shields.io/badge/compression-up%20to%2086%25-blue" alt="86% compression"> <img src="https://img.shields.io/badge/dependencies-5%20prod-lightgrey" alt="5 deps"> <img src="https://img.shields.io/npm/l/mcp-slim-guard" alt="MIT"> </p>
<br>
mcp-slim-guard sits between your AI agent and MCP servers, transparently adding schema compression (5 levels, up to 86% token reduction) and security policies (SSRF protection, tool allow/deny, injection detection, rate limiting, audit logging).
graph LR
A[AI Agent] --> B[mcp-slim-guard]
B --> C[Compression Pipeline]
B --> D[Security Pipeline]
C --> E[MCP Server 1]
C --> F[MCP Server N]
D --> E
D --> F
style B fill:#4a90d9,color:#fff
style C fill:#e6f3ff
style D fill:#ffe6e6
Why mcp-slim-guard?
| Problem | Impact | How mcp-slim-guard solves it |
|---|---|---|
| Context wasted | Tool schemas eat 60-86% of your context window | 5 compression levels, lazy loading, request cache |
| No access control | Any agent calls any tool with any args | Glob-based allow/deny, fail-closed by default |
| SSRF | Tool params inject internal network requests | IP blacklist + domain whitelist |
| Prompt injection | Malicious params execute shell/SQL | 17 heuristic patterns, 3 sensitivity levels |
| Abuse | Unthrottled tool calls flood upstream | Token bucket rate limiter (per-tool configurable) |
| No audit trail | No record of who called what | Structured JSON audit log with rotation + gzip |
Only mcp-slim-guard combines compression AND security in a single proxy.
Other tools compress schemas but don't protect you. Security proxies don't save you tokens.
Quick Start
# Install
npm install -g mcp-slim-guard
# Auto-discover MCP servers from your .mcp.json
cd your-project/
mcp-slim-guard init
# Dry-run policies to check for false positives
mcp-slim-guard validate
# Start the proxy
mcp-slim-guard start
Your agent now connects to mcp-slim-guard instead of the original servers. That's it.
MCP servers are auto-discovered from
.mcp.json,mcp.json, orclaude_desktop_config.json.
Generated mcp-slim-guard.yml
tools:
allow: ["search_*", "read_*"] # only allow search/read tools
deny: ["*_delete_*", "*_admin_*"] # block dangerous ops
ssrf:
mode: block
block_private_ips: true
allow_domains: ["*.github.com"]
rate_limit:
default: 60/min # per-tool rate limit
injection_detection:
enabled: true
mode: block
sensitivity: medium
compressor:
enabled: true
level: light # 5 levels: off/light/normal/extreme/maximum
cache:
enabled: false # TTL+LRU read-only response cache
audit:
output: file # structured JSON audit log
maxSize: 10MB
maxFiles: 5
Features
🗜️ Schema Compression — Reclaim Your Context Window
| Level | Strategy | Tokens (14 tools) | Savings | When to use |
|---|---|---|---|---|
off |
Passthrough | 1,736 | — | < 5 tools, or testing |
light |
3 wrapper tools (on-demand schema) | 300 | -83% | ⭐ Default. Best balance for most users |
normal |
2 wrapper tools (no list_tools) | 245 | -86% | 30+ tools, strong LLMs |
extreme |
In-place: strip property descriptions | 1,361 | -22% | Few tools with complex schemas (10+ params) |
maximum |
In-place: signature only, empty properties | 1,294 | -25% | Very large individual schemas |
lazy |
Budget preload + on-demand schema | 1,644 | -5% | 30+ tools, most used only occasionally |
Why the big gap?
light/normalreplace all tools with 2-3 wrapper tools (mcp__invoke_tool,mcp__get_tool_schema). The LLM fetches schemas on demand.extreme/maximumkeep all tools and only compress each schema in place — savings depend on schema complexity.
Real-world cost impact
| Setup | Tokens/call | Monthly cost (DeepSeek V4) |
|---|---|---|
| Without compression | 1,736 | ~$52 (10K calls) |
With light |
300 | ~$9 (-83%) |
Accuracy verified
Benchmarked against DeepSeek V4 Flash across 12 scenarios × 5 levels × 3 runs = 180 API calls.
Run it yourself →
🛡️ Security Pipeline — Defense in Depth
Every tool call runs through a serial pipeline. First rejection stops execution:
Agent Request
│
▼
┌─────────────────┐
│ 1. Allow/Deny │ ← Glob pattern matching. Fail-closed.
│ (Whitelist) │
└────────┬────────┘
▼
┌─────────────────┐
│ 2. SSRF Shield │ ← IP blacklist + domain whitelist.
│ │ Blocks 10.*, 192.168.*, 169.254.*
└────────┬────────┘
▼
┌─────────────────┐
│ 3. Injection │ ← 17 heuristic patterns:
│ Detection │ Shell/SQL/NoSQL/Prompt injection
└────────┬────────┘
▼
┌─────────────────┐
│ 4. Rate Limit │ ← Token bucket. Per-tool or global.
│ │ Default: 60 req/min/tool
└────────┬────────┘
▼
┌─────────────────┐
│ 5. Audit Log │ ← Structured JSON. Rotate + compress.
│ │ Every decision recorded.
└────────┬────────┘
▼
Upstream MCP Server
🔄 Additional Capabilities
| Feature | Description |
|---|---|
| Multi-server routing | One proxy, multiple upstream MCP servers. Tool names are prefixed ({server}_{tool}) for automatic routing. |
| Hot reload | kill -HUP <pid> — zero-downtime config reload. All fields hot-swappable. |
| Request cache | TTL+LRU in-memory cache for read-only tool results. Per-tool stats. |
| Streamable HTTP | mcp-slim-guard start --http --port 3000 — works as a remote MCP endpoint. |
| STDIO mode | Default. Transparent drop-in for local agents. |
How It Works
sequenceDiagram
participant LLM as AI Agent
participant MM as mcp-slim-guard
participant US as Upstream MCP Server
LLM->>MM: tools/list
Note over MM: Compression pipeline<br/>filters + transforms tools
MM-->>LLM: compressed tool list (light: 3 wrappers)
LLM->>MM: mcp__get_tool_schema("search")
MM-->>LLM: full schema for "search"
LLM->>MM: mcp__invoke_tool("search", {q: "..."})
Note over MM: Security pipeline<br/>whitelist → ssrf → injection → ratelimit
MM->>US: forward call
US-->>MM: result
Note over MM: Audit log entry
MM-->>LLM: result
CLI Reference
| Command | Description |
|---|---|
mcp-slim-guard init |
Auto-discover .mcp.json, generate mcp-slim-guard.yml |
mcp-slim-guard validate |
Dry-run policies, show allow/deny for each tool |
mcp-slim-guard start |
Start proxy (STDIO mode) |
mcp-slim-guard start --http --port 3000 |
Start proxy (HTTP mode) |
mcp-slim-guard status |
Show config summary + policy overview |
mcp-slim-guard doctor |
Diagnose upstream server connectivity |
mcp-slim-guard audit |
View audit log |
mcp-slim-guard uninit |
Remove mcp-slim-guard.yml and roll back |
Benchmarks
All benchmarks use real MCP server tool schemas (filesystem server, 14 tools) with tiktoken (gpt-4o encoding).
Run them yourself: npm run bench
Token Savings
| Level | Tokens | Reduction |
|---|---|---|
| off | 1,736 | baseline |
| light | 300 | -83% |
| normal | 245 | -86% |
| extreme | 1,361 | -22% |
| maximum | 1,294 | -25% |
| lazy | 1,644 | -5% |
Latency Overhead
Policy pipeline: ~2ms/call (whitelist → ssrf → injection → ratelimit)
Compression (light): <0.05ms
Cache hit: 0.01ms
Accuracy (DeepSeek V4 Flash)
12 scenarios × 5 levels × 3 runs = 180 API calls. Scenarios include 4 fuzzy-name tests (read vs search, list vs tree).
| Level | Accuracy | Notes |
|---|---|---|
| off | 100% | Baseline |
| light | ✅ (on-demand) | Wrapper mode uses extra round-trip |
| normal | ✅ (on-demand) | Same as light |
Comparison
| Feature | mcp-slim-guard | slim-mcp | mcp-compressor | mcp-guardian |
|---|---|---|---|---|
| Schema compression | ✅ 5 levels, -86% | ✅ 5 levels, -77% | ✅ | ❌ |
| Accuracy validation | ✅ 180 API calls | ✅ 120 API calls | ❌ | — |
| Request cache | ✅ TTL+LRU | ❌ | ❌ | ❌ |
| Tool allow/deny | ✅ Glob-based | ❌ | ❌ | ✅ |
| SSRF protection | ✅ IP + domain | ❌ | ❌ | ✅ |
| Injection detection | ✅ 17 patterns | ❌ | ❌ | ✅ |
| Rate limiting | ✅ Token bucket | ❌ | ❌ | ✅ |
| Audit log | ✅ JSON, rotation | ❌ | ❌ | ✅ |
| Hot reload | ✅ SIGHUP | ❌ | ❌ | ❌ |
| Multi-server routing | ✅ Prefix auto-route | ❌ | ❌ | ❌ |
| HTTP transport | ✅ Streamable HTTP | ❌ | ✅ | ❌ |
| Compression + Security | ✅ One proxy | ❌ Compression only | ❌ Compression only | ❌ Security only |
Requirements
- Node.js >= 18
- Only 5 production dependencies (MCP SDK, commander, js-yaml, micromatch, pino)
Docker
docker build -t mcp-slim-guard .
docker run -i --rm -v $(pwd)/mcp-slim-guard.yml:/app/mcp-slim-guard.yml mcp-slim-guard start
License
MIT
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。
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。
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。
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。