Secure grip for your agent's secrets - security-hardened MCP gateway with proxy token architecture
Secure grip for your agent's secrets - security-hardened MCP gateway with proxy token architecture. Exposed over MCP by the pincer mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
Everything the assistant can do here goes through one of these:
gemini_generate — Secure Google Gemini API callsopenai_chat — Chat completions with OpenAI GPT models (gpt-4o, gpt-4-turbo, gpt-3.5-turbo, etc.)openai_list_models — List all available OpenAI modelsopenai_compatible_chat — Chat completions with any OpenAI-compatible API (Azure OpenAI, Ollama, vLLM, etc.)openai_compatible_list_models — List models from custom OpenAI-compatible endpointsclaude_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 providersopenwebui_chat — OpenAI-compatible interface for self-hosted LLMsopenwebui_list_models — Discover available models on an OpenWebUI instancegpg_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 keyYou will need one environment variable: PINCER_PROXY_TOKEN. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Being a remote server, there is no local install. You register the endpoint with your client, authorise it once, and the tools appear.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Pincer's toolset — gemini_generate, openai_chat, openai_list_models and 11 more — is a fair guide to whether it matches your workflow. It is maintained by VouchlyAI; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Pincer's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| 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. |
| Prerequisites | The Prerequisites tool exposed by this server. |
| Installation | The Installation tool exposed by this server. |
{
"mcpServers": {
"pincer": {
"command": "npx",
"args": ["-y", "pincer-mcp"],
"env": {
"PINCER_PROXY_TOKEN": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| PINCER_PROXY_TOKEN | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.