Embedded Docs is answering right now. Last checked 8 min ago. It exposes 3 tools. Last commit 20 Jun 2026.
Page-cited retrieval for embedded docs, datasheets, MISRA, CMSIS, and RTOS references.
Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 20 September 2026. No other catalogue keeps this.
We read the source, 14 h ago · tools taken from the live server · rules 3dff92dd89df
What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.
subprocess.run(["ffmpeg", "-y", "-v", "error", "-i", os.path.join(tmp, "f%04d.png"),
frame(i / FPS * 1000).save(os.path.join(tmp, f"f{i:04d}.png"))
Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.
Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 8 min ago.
claude mcp add embedded-docs --transport http https://mcp.byteask.ai/mcp
{
"mcpServers": {
"embedded-docs": {
"url": "https://mcp.byteask.ai/mcp"
}
}
}
[mcp_servers.embedded-docs]
url = "https://mcp.byteask.ai/mcp"
{
"mcpServers": {
"embedded-docs": {
"url": "https://mcp.byteask.ai/mcp"
}
}
}
{
"mcpServers": {
"embedded-docs": {
"url": "https://mcp.byteask.ai/mcp"
}
}
}
Read directly from the server with tools/list, grouped by what they act on.
If a tool disappears, we record the date.
get_context
search_docs
request_document
| URL | Transport | State | Latency | Checked |
|---|---|---|---|---|
| https://mcp.byteask.ai/mcp | streamable-http | answering | 603 ms | 8 min ago |
Embedded, local-first memory and retrieval for AI agents. One SQLite file, no server, no API key.
Verified doc corpora for agents: grep-first retrieval, hashed pages, Merkle+RFC-3161 receipts
MCP server for querying and retrieving Qiskit documentation, guides, and API references
The open retrieval layer for AI agents — index code, docs, data. Search via MCP.
Shared, versioned knowledge for AI coding agents: cited retrieval and publishing of OKF bundles.
Ingest, manage, and retrieve documents for RAG-powered AI applications
Living docs and MCP context for GitHub repos — conventions, gaps, and source-cited pages on merge.
CUDA-Q docs, API reference, and runnable examples for AI agents, pinned to your installed version.
Answers built from our own checks of this server.