MCP server

Embedding Search

io.github.lazymac2x/embedding-search

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Cloudflare Workers MCP server: embedding-search

Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

3

Token cost

About 360 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http embedding-search https://api.lazy-mac.com/embedding-search/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.embedding-search]
url = "https://api.lazy-mac.com/embedding-search/mcp"

Gemini CLI

{"mcpServers":{"embedding-search":{"httpUrl":"https://api.lazy-mac.com/embedding-search/mcp"}}}

Cursor

{"mcpServers":{"embedding-search":{"url":"https://api.lazy-mac.com/embedding-search/mcp"}}}

VS Code

{"servers":{"embedding-search":{"type":"http","url":"https://api.lazy-mac.com/embedding-search/mcp"}}}

Antigravity

agy mcp add embedding-search https://api.lazy-mac.com/embedding-search/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"embedding-search","server_url":"https://api.lazy-mac.com/embedding-search/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"embedding-search","server_url":"https://api.lazy-mac.com/embedding-search/mcp"}

Tools

• compute_similarity ~88 tokens
  Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness.
• generate_embeddings ~119 tokens
  Generate vector embeddings for one or more texts using Cloudflare Workers AI (bge-base-en-v1.5, 768-dim).
• semantic_search ~153 tokens
  Rank a list of documents against a query using cosine similarity of bge embeddings. Returns top-k matches with scores.

Browse

• GitHub tools
• Cloudflare tools
• GitHub and code hosting
• Cloud and hosting
• Searching your documents (RAG)

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