Pharo Smalltalk implementation of Anthropic's Model Context Protocol (MCP) server specification. Enables LLMs like Claude to interact with Pharo
Connect Pharo to Claude, Cursor or any other MCP client and it stops being a tab you switch to. Pharo Smalltalk implementation of Anthropic's Model Context Protocol (MCP) server specification. Enables LLMs like Claude to interact with Pharo applications. The pharo mcp server is what makes that connection.
The Model Context Protocol (MCP), developed by Anthropic, is a specification designed to standardize how Large Language Models (LLMs) interact with external tools and services. It defines mechanisms for:
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
Extensible — ** Designed to be integrated into larger Pharo applicationsAmong 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. Pharo's toolset — Extensible — is a fair guide to whether it matches your workflow. It is maintained by paulwilke; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Extensible | ** Designed to be integrated into larger Pharo applications. |
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.