The idea is that the LLM will be able to generate and evaluate arbitrary Lisp expressions, including creating, compiling, loading, and testing entire
The idea is that the LLM will be able to generate and evaluate arbitrary Lisp expressions, including creating, compiling, loading, and testing entire files and projects. Exposed over MCP by the lisply mcp mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
You will need 2 environment variables: BACKEND_HOST, LISPLY_BACKEND_HOST. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
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. It is maintained by gornskew; 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.
{
"mcpServers": {
"gendl-ccl": {
"command": "node",
"args": [
"/path/to/cloned/lisply-mcp/scripts/mcp-wrapper.js",
"--server-name", "gendl-ccl",
"--http-port", "9080"
]
}
},
{
"gendl-sbcl": {
"command": "node",
"args": [
"/path/to/cloned/lisply-mcp/scripts/mcp-wrapper.js",
"--server-name", "gendl-sbcl",
"--http-port", "9090"
]
}
},
{
"skewed-emacs": {
"command": "node",
"args": [
"/path/to/cloned/lisply-mcp/scripts/mcp-wrapper.js",
"--server-name", "skewed-emacs",
"--http-port", "7080"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| BACKEND_HOST | Endpoint or connection string the server talks to. | Optional |
| LISPLY_BACKEND_HOST | Endpoint or connection string the server talks to. | Optional |
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.