Tiny MCP server with cryptography tools, sufficient to establish end-to-end encryption between LLM agents
Tiny MCP server with cryptography tools, sufficient to establish end-to-end encryption between LLM agents. Exposed over MCP by the tiny cryptography mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
A Model Context Protocol server built with Express.js that provides cryptographic tools including key pair generation, shared secret derivation, and message encryption/decryption.
The Model Context Protocol (MCP) is an open standard that defines how AI models and tools communicate. It enables seamless interoperability between language models and external capabilities, allowing AI systems to use tools more effectively. MCP standardizes the way models request information and actions, making it easier to build complex AI applications with multiple components.
Everything the assistant can do here goes through one of these:
Messages — The Messages tool exposed by this serverSetup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.
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. Tiny Cryptography's toolset — Messages — is a fair guide to whether it matches your workflow. It is maintained by anton10xr; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Tiny Cryptography's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| Messages | The Messages tool exposed by this server. |
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.