embedded docs for LangChain
ai.byteask/embedded-docs
Page-cited retrieval for embedded docs, datasheets, MISRA, CMSIS, and RTOS references.
client:LangChain
transport:streamable-http
tools:3
Install embedded docs in LangChain
pip install langchain-mcp-adapters
from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient({
"embedded-docs": {
"transport": "streamable_http",
"url": "https://mcp.byteask.ai/mcp",
}
})
tools = await client.get_tools()
get_tools() returns the server's tools as LangChain tools, ready for an agent. LangChain docs