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Embedding Search MCP Server

answering

Embedding Search is answering right now. Last checked 11 min ago. It exposes 3 tools.

Cloudflare Workers MCP server: embedding-search

Uptime history 47 days of history
47 days agonow
100.0%
Uptime 24h
91 of 91 checks
3
Tools
read from the server
171 ms
Response time
average over 24h
0
Stars
on GitHub

Nothing serious here today

Today is the operative word: we check Embedding Search every 15 minutes and re-read its code on every release. Watch it and you find out the day that stops being true.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 11 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 155 ms 11 min ago

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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 91 of 91 checks got a reply (100.0%), average response time 171 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 171 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?
We cannot say either way: 0 stars on GitHub, but we could not determine the licence, and without one the code is not open source by default.