
Pincer
io.github.VouchlyAI由 GitHub 帳號 VouchlyAI 發布v0.1.5更新於 Oct 24, 2026
Secure grip for your agent's secrets - security-hardened MCP gateway with proxy token architecture
概覽
本機 MCP 閘道,讓助理以代理權杖而非真實 API 金鑰呼叫大型語言模型服務,並可簽章或解密資料。
- 可以做什麼
- Pincer 以本機 stdio MCP 伺服器形式執行,代替代理程式呼叫外部服務。工具包含 gemini_generate、openai_chat、openai_list_models、openai_compatible_chat、openai_compatible_list_models、claude_chat、openrouter_chat、openrouter_list_models、openwebui_chat、openwebui_list_models、gpg_sign_data 與 gpg_decrypt。代理程式以代理權杖驗證,Pincer 將其解析為存放於作業系統鑰匙圈中加密保存的真實憑證,在呼叫當下即時注入,並把每次呼叫寫入具雜湊鏈的稽核檔案。
- 適用情境
- 當助理需要存取多個大型語言模型供應商,或需要執行 GPG 簽章而不想自行持有長期 API 金鑰與私鑰時適用。也適合需要依代理程式、依工具授權並保留工具呼叫稽核紀錄的情境。
- 執行需求
- 需要 Node.js 18+ 以及支援原生鑰匙圈的作業系統(macOS、Windows 或 Linux)。安裝 npm 套件 pincer-mcp,執行 pincer init 建立保管庫,存入供應商金鑰,並註冊代理程式以取得代理權杖。必須向代理程式提供 PINCER_PROXY_TOKEN;選用變數為 AUDIT_LOG_PATH、OPENAI_COMPATIBLE_URL 與 OPENWEBUI_URL。呼叫外部供應商需要網路存取。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Pincer,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
Pincer MCP 🦀
[License] [TypeScript] [MCP] [NPM Version] [NPM Downloads] [MCP Registry]
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
📦 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
(More callers coming soon!)
🚀 Quick Start
Prerequisites
- Node.js 18+
- macOS, Windows, or Linux with native keychain support
Installation
Option 1: Global Installation (Recommended)
Option 2: Local Development
Setup Vault
Multi-Key Support
Store multiple keys for the same tool and assign them to different agents:
Result: clawdbot uses key1, mybot uses key2 - perfect for rate limiting or cost tracking!
Run the Server
Configure Your Agent
Give your agent the proxy token (not the real API key):
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:
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
🔐 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:
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.
📚 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.
來源:README.md,提交 5ca44e7
工具
0版本歷史
1- v0.1.5最新Sep 16, 2026
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