An ergonomic **stdio MCP facade** over WeCom's official **robot-doc MCP** backend. It proxies all 25 backend tools verbatim and adds a transform
An ergonomic stdio MCP facade over WeCom's official robot-doc MCP backend. It proxies all 25 backend tools verbatim and adds a transform layer that makes the raw output usable by LLM agents:. The wecom docs mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 2 defined tools rather than through you.
wecom-docs-mcp-server on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Everything the assistant can do here goes through one of these:
Domain — Readdoc — get_doc_contentYou will need 2 environment variables: WECOM_MCP_APIKEY, WECOM_MCP_BASE_URL. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
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. Wecom Docs's toolset — Domain, doc — is a fair guide to whether it matches your workflow. It is maintained by beltran12138; 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We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Domain | Read |
| doc | get_doc_content |
{
"mcpServers": {
"wecom-doc": {
"command": "wecom-docs-mcp-server",
"env": { "WECOM_MCP_APIKEY": "your_apikey_here" }
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| WECOM_MCP_APIKEY | Credential the server authenticates with. | Yes |
| WECOM_MCP_BASE_URL | Endpoint or connection string the server talks to. | 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.