Embedding Search

lazymac2x/embedding-search-api

The repository appears to be empty or freshly initialized, so details are sparse. Based on the naming and remote endpoint, this looks like a semantic search server running on Cloudflare Workers that lets Claude query embeddings to find similar content. You'd typically use this when you need to search unstructured data by meaning rather than keywords, like finding relevant documents, matching user queries to a knowledge base, or building recommendation features. The streamable HTTP transport means it works with Claude Desktop and other MCP clients that support remote connections. Without source code or documentation visible, you're taking it on faith that the API at api.lazy-mac.com handles the embedding generation and vector similarity search under the hood.

tools: 3
transport: HTTP
last updated: May 16, 2026

View on GitHub

Install

From registry metadata

$ npx add-mcp https://api.lazy-mac.com/embedding-search/mcp --name embedding-search

Run in your terminal. add-mcp asks which agents to add it to (Claude Code, Codex, Cursor and more). Add -g to install it for every project.

Review the command, arguments and environment values before installing. MCP servers run with your local permissions.

Other ways

Claude Code

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

Run in your terminal. Add --scope user to make it available in every project.

Tools

Verified live · 3 tools

Verified live against the running server on Nov 6, 2026.

generate_embeddingsGenerate vector embeddings for one or more texts using Cloudflare Workers AI (bge-base-en-v1.5, 768-dim).2 params

Generate vector embeddings for one or more texts using Cloudflare Workers AI (bge-base-en-v1.5, 768-dim).

Parameters* required

modelstring

CF AI model ID override

text*value

Text or array of texts to embed

semantic_searchRank a list of documents against a query using cosine similarity of bge embeddings. Returns top-k matches with scores.4 params

Rank a list of documents against a query using cosine similarity of bge embeddings. Returns top-k matches with scores.

Parameters* required

documents*array

Candidate documents to rank

modelstring

CF AI model ID override

query*string

Search query

top_kinteger

Number of results (default 5)

compute_similarityCompute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness.3 params

Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness.

Parameters* required

modelstring

text_a*string

text_b*string

Readme

Details

Repository: github.com/lazymac2x/embedding-search-api
Categories: AI & LLM Tools, Cloud & Infrastructure, Search & Web Crawling
Transport: HTTP
Registry: MCP registry, active
Tools verified: Nov 6, 2026

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Embedding Search

Install

Tools

Readme

Details

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