Pincer-MCP
A security-hardened MCP gateway that enables AI agents to call LLM APIs (Gemini, OpenAI, Claude, etc.) using ephemeral proxy tokens, eliminating exposure of real API keys.
README
Pincer MCP 🦀
<p align="center"> <picture> <source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/VouchlyAI/Pincer-MCP/refs/heads/main/mascot.png"> <img src="https://raw.githubusercontent.com/VouchlyAI/Pincer-MCP/refs/heads/main/mascot.png" alt="Pincer-MCP" width="500"> </picture> </p>
Pincer-MCP is a security-hardened Model Context Protocol (MCP) gateway that eliminates the "Lethal Trifecta" vulnerability in agentic AI systems. By acting as a stateless intermediary, Pincer ensures agents never see your real API keys.
🔒 The Problem
Current AI agents store long-lived API keys in plain-text .env files or local databases. If compromised via prompt injection or host intrusion, attackers gain direct access to your:
- Database passwords
- Third-party API keys
✨ The Solution: Proxy Token Architecture
Pincer implements a "blindfold" security model:
- Agent knows: Only a unique proxy token (
pxr_abc123...) - Pincer knows: Mapping of proxy tokens → real API keys (encrypted in OS keychain)
- Agent never sees: The actual credentials
sequenceDiagram
participant Agent
participant Pincer
participant Vault (OS Keychain)
participant External API
Agent->>Pincer: tools/call + proxy_token: pxr_abc123
Pincer->>Vault: Decrypt real API key
Vault-->>Pincer: gemini_api_key: AIzaSy...
Pincer->>External API: API call with real key
External API-->>Pincer: Response
Pincer->>Pincer: Scrub key from memory
Pincer-->>Agent: Response (no credentials)
📦 Available Tools
gemini_generate: Secure Google Gemini API calls.openai_chat: Chat completions with OpenAI GPT models (gpt-4o, gpt-4-turbo, gpt-3.5-turbo, etc.).openai_list_models: List all available OpenAI models.openai_compatible_chat: Chat completions with any OpenAI-compatible API (Azure OpenAI, Ollama, vLLM, etc.).openai_compatible_list_models: List models from custom OpenAI-compatible endpoints.claude_chat: Chat completions with Anthropic Claude models (Claude 3.5 Sonnet, Opus, Haiku).openrouter_chat: Unified API access to 100+ models from multiple providers (OpenAI, Anthropic, Google, Meta, etc.).openrouter_list_models: List all available models across OpenRouter providers.openwebui_chat: OpenAI-compatible interface for self-hosted LLMs.openwebui_list_models: Discover available models on an OpenWebUI instance.gpg_sign_data: Sign data or files using a GPG/PGP private key stored in Pincer's vault. (Keyless Execution — agent never sees the key)gpg_decrypt: Decrypt PGP-encrypted data using a vault-stored private key.
🔑 GPG Key Management
# Generate a new GPG keypair (private key stored in vault)
pincer key generate --name "Release Signing" --email dev@example.com
# Import an existing PGP private key
pincer key import ./my-key.asc --passphrase "my-passphrase"
# List all stored GPG keys
pincer key list
# Export public key (safe to share)
pincer key export <key-id>
# Authorize an agent for signing
pincer agent authorize mybot gpg_sign_data --key <key-id>
(More callers coming soon!)
🚀 Quick Start
Prerequisites
- Node.js 18+
- macOS, Windows, or Linux with native keychain support
Installation
Option 1: Global Installation (Recommended)
npm install -g pincer-mcp
# Now 'pincer' command is available system-wide
Option 2: Local Development
git clone https://github.com/VouchlyAI/Pincer-MCP.git
cd Pincer-MCP
npm install
npm run build
npm link # Makes 'pincer' command available locally
Setup Vault
# 1. Initialize vault (creates master key in OS keychain)
pincer init
# 2. Store your real API keys (encrypted)
pincer set gemini_api_key "AIzaSyDpxPq..."
pincer set openai_api_key "sk-proj-..."
# 3. Register an agent and generate proxy token
pincer agent add openclaw
# Output: 🎫 Proxy Token: pxr_V1StGXR8_Z5jdHi6B-myT
# 4. Authorize the agent for specific tools
pincer agent authorize openclaw gemini_generate
Multi-Key Support
Store multiple keys for the same tool and assign them to different agents:
# Store two different Gemini API keys
pincer set gemini_api_key "AIzaSy_KEY_FOR_CLAWDBOT..." --label key1
pincer set gemini_api_key "AIzaSy_KEY_FOR_MYBOT..." --label key2
# View all stored keys
pincer list
# Assign specific keys to each agent
pincer agent add clawdbot
pincer agent authorize clawdbot gemini_generate --key key1
pincer agent add mybot
pincer agent authorize mybot gemini_generate --key key2
# View agent permissions
pincer agent list
Result: clawdbot uses key1, mybot uses key2 - perfect for rate limiting or cost tracking!
Run the Server
npm run dev
Configure Your Agent
Give your agent the proxy token (not the real API key):
export PINCER_PROXY_TOKEN="pxr_V1StGXR8_Z5jdHi6B-myT"
Tool-to-Secret Name Mappings
When storing secrets, you must use the correct secret name for each tool. See the Tool Mappings Guide for a complete reference.
When you run pincer agent authorize myagent gemini_generate, Pincer will inject the gemini_api_key secret when that tool is called.
Make a Tool Call
Your agent sends requests with the proxy token in the body:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "gemini_generate",
"arguments": {
"prompt": "Hello world",
"model": "gemini-2.0-flash"
},
"_meta": {
"pincer_token": "pxr_V1StGXR8_Z5jdHi6B-myT"
}
}
}
Pincer maps the proxy token to the real API key and executes the call securely.
🏗️ Architecture
Two-Tiered Vault System
Tier 1: Master Key (OS Keychain)
- Stored in macOS Keychain, Windows Credential Manager, or GNOME Keyring
- Never touches the filesystem
- Accessed only for encryption/decryption
Tier 2: Encrypted Store (SQLite)
- Database at
~/.pincer/vault.db - Three tables:
secrets: Real API keys (AES-256-GCM encrypted)proxy_tokens: Proxy token → Agent ID mappingsagent_mappings: Agent ID → Tool authorization
Authentication Flow
Request (_meta.pincer_token: pxr_xxx)
↓
Gatekeeper: Extract proxy token from body
↓
Vault: Resolve pxr_xxx → agent_id → tool_name → real_api_key
↓
Injector: JIT decrypt & inject real key
↓
Caller: Execute external API call
↓
Scrubber: Overwrite key in memory with zeros
↓
Audit: Log to tamper-evident chain
🔐 Security & Compliance
Pincer is built for enterprise-grade security:
- Hardware-Backed Cryptography: Master encryption keys never leave the OS-native keychain.
- Proxy Token Isolation: Agents only handle ephemeral
pxr_tokens; they never touch real credentials. - JIT Decryption: Secrets are decrypted only for the duration of the API call.
- Zero-Footprint Memory: Sensitive data is scrubbed (zeroed out) from memory immediately after use.
- Fine-Grained Authorization: Strict per-agent, per-tool access control policies.
- Tamper-Evident Audit Log: Append-only tool call history with SHA-256 chain-hashing.
- Hardened Execution: Schema validation on all inputs and protected environment execution.
- Stdio Compatible: Fully compatible with the standard Model Context Protocol transport.
🔍 Audit Logs
Every tool call is logged to ~/.pincer/audit.jsonl with both UTC and Local timestamps, plus character counts and estimated token usage:
{
"agentId": "openclaw",
"tool": "gemini_generate",
"duration": 234,
"status": "success",
"input_chars": 156,
"output_chars": 423,
"estimated_input_tokens": 39,
"estimated_output_tokens": 106,
"timestamp_utc": "2026-02-05T08:32:00.000Z",
"timestamp_local": "2/5/2026, 2:02:45 PM",
"chainHash": "a1b2c3d4e5f6g7h8",
"prevHash": "0000000000000000"
}
Token Estimation: Pincer automatically estimates token usage using a 4:1 character-to-token ratio (~4 characters per token average). This provides consistent cost tracking across all AI providers without relying on provider-specific APIs.
Chain hashes provide tamper detection - any modification breaks the SHA-256 chain.
## 🧪 Development
```bash
# Install dependencies
npm install
# Run tests
npm test
# Run with watch mode
npm run dev
# Build for production
npm run build
📚 Documentation
- Setup Guide - Getting started with Pincer-MCP
- IDE Integration - Use Pincer with VSCode, Claude Desktop, Cursor, and more
- OpenClaw Integration - Integrate Pincer with OpenClaw agents
- Testing Guide - Comprehensive test suite documentation
- Capabilities Reference - Full API and feature documentation
- Security Policy - Vulnerability reporting and security best practices
- CHANGELOG - Version history and release notes
🤝 Contributing
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
📄 License
BSL 1.1 (Business Source License) — See LICENSE for details. Converts to Apache 2.0 on 2028-04-01.
- Model Context Protocol - The standard for AI tool integration.
- keytar - Secure cross-platform keychain access.
- better-sqlite3 - High-performance local persistence.
Built with ❤️ for a more secure AI future.
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。