Feishu/Lark OpenAPI MCP
Feishu/Lark OpenAPI MCP. That is what the lar openai mcp mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
This is the Feishu/Lark official OpenAPI MCP (Model Context Protocol) tool designed to help users quickly connect to the Feishu/Lark platform and enable efficient collaboration between AI Agents and Feishu/Lark. The tool encapsulates Feishu/Lark Open Platform API interfaces as MCP tools, allowing AI assistants to directly call these interfaces and implement various automation scenarios such as document processing, conversation management, calendar scheduling, and more.
@larksuiteoapi/lark-mcp on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Configuration is passed through the environment: APP_ID, APP_SECRET. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. It is maintained by KAIpany; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
{
"mcpServers": {
"lar-openai": {
"command": "npx",
"args": ["-y", "@larksuiteoapi/lark-mcp"],
"env": {
"APP_ID": "your-value",
"APP_SECRET": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| APP_ID | Configuration value read at startup. | Optional |
| APP_SECRET | 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.