mcpbeat

Embedding Search MCP Server

io.github.lazymac2x/embedding-search
answering

Embedding Search is answering right now. Last checked 1 min ago. It exposes 3 tools.

Cloudflare Workers MCP server: embedding-search

Uptime history 39 hours of history
39 hours agonow
100.0%
Uptime 24h
92 of 92 checks
3
Tools
read from the server
155 ms
Response time
average over 24h
0
Stars
on GitHub

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 1 min ago.

run in your terminal
claude mcp add embedding-search --transport http https://api.lazy-mac.com/embedding-search/mcp
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "embedding-search": {
      "url": "https://api.lazy-mac.com/embedding-search/mcp"
    }
  }
}
~/.codex/config.toml
[mcp_servers.embedding-search]
url = "https://api.lazy-mac.com/embedding-search/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "embedding-search": {
      "url": "https://api.lazy-mac.com/embedding-search/mcp"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "embedding-search": {
      "url": "https://api.lazy-mac.com/embedding-search/mcp"
    }
  }
}

Available tools 3

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

compute
compute_similarity
Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness.
generate
generate_embeddings
Generate vector embeddings for one or more texts using Cloudflare Workers AI (bge-base-en-v1.5, 768-dim).
semantic
semantic_search
Rank a list of documents against a query using cosine similarity of bge embeddings. Returns top-k matches with scores.

Endpoints

URLTransportStateLatencyChecked
https://api.lazy-mac.com/embedding-search/mcp streamable-http answering 216 ms 1 min ago

Embedding Search — questions

Answers built from our own checks of this server.

What can Embedding Search do?
It exposes 3 tools, read directly from the server on our last check. Among them: compute_similarity, generate_embeddings, semantic_search. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
Is Embedding Search working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 92 of 92 checks got a reply (100.0%), average response time 155 ms. The bar chart above shows every period we have measured.
How do I connect Embedding Search?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Embedding Search need an API key?
No. Embedding Search completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 3 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Embedding Search?
It answers our handshake in 155 ms on average, which is faster than 72% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.
Is Embedding Search open source?
Yes — 0 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.