MCP server that serves the Alchemy ruleset to any agent, so it writes human-quality prose instead of tell-tale LLM text.
MCP server that serves the Alchemy ruleset to any agent, so it writes human-quality prose instead of tell-tale LLM text. Exposed over MCP by the alchemy mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
Alchemy is a drop-in ruleset that stops your AI coding agent from writing like an LLM: no em-dash pile-ups, no "not just X, it's Y", no stray delve. You add it once, your agent reads it before it writes, and the READMEs, commits, and docs it produces start sounding like a person wrote them.
Everything the assistant can do here goes through one of these:
Throat — clearing openers ("In today's fast-paced world", "At its core") and recap closers ("In conclusion", "Ultimately")PyPI — alchemy-writing — pip install alchemy-writingMCP — @fernforge/alchemy-mcp — npx @fernforge/alchemy-mcp@fernforge/alchemy on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Alchemy's toolset — Throat, PyPI, MCP — is a fair guide to whether it matches your workflow. It is maintained by fernforge; 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 |
|---|---|
| Throat | clearing openers ("In today's fast-paced world", "At its core") and recap closers ("In conclusion", "Ultimately"). |
| PyPI | alchemy-writing — pip install alchemy-writing |
| MCP | @fernforge/alchemy-mcp — npx @fernforge/alchemy-mcp |
{
"mcpServers": {
"alchemy": { "command": "npx", "args": ["-y", "@fernforge/alchemy-mcp"] }
}
}Configuration as documented by the project. Restart the client after saving.
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.