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Jina AI Multimodal Search

Semantic, image and cross-modal search over a Jina AI collection.

by Sheshiyer ↗Vector Stores & RAG RetrievalCommunity

LABSCO SUMMARY

It searches collections; it does not build them.

Every tool takes a collection name, which means the indexing work happens on the Jina side before this is useful at all — connect it to an existing collection rather than expecting it to ingest your data. Once that is true, cross-modal is the capability worth having: describing what you are looking for in words and getting images back is not something a keyword index can do.

What it is

A search client for Jina AI's neural search. Three tools query a named collection: by meaning over text, by visual similarity from an image URL, and across the two — text describing an image, or an image finding text.

What you get

  • semantic_search takes a query, a collection and an optional limit (default 10)
  • image_search takes an imageUrl and finds visually similar images in the same collection
  • cross_modal_search takes a mode of text2image or image2text, so a sentence can find pictures or a picture can find text
  • Every result carries an id, a numeric score and its data, so ranking is visible rather than implied
  • Errors are typed and returned rather than thrown — invalid keys, bad parameters, rate limits, network failures and unknown collection names each come back with a code

Requirements

Node.js 16 or higher, and a Jina AI account with an API key from https://cloud.jina.ai/. Published as jina-ai-mcp version 1.0.0; the documented path is clone, npm install, put JINA_API_KEY in a .env file, npm run build, then point your client at build/index.js with the key in the entry's env. Every call names a collection, so the collections have to exist on the Jina side first — this server searches them, it does not create or index them.

Setup effort

Build from source — git clone https://github.com/Sheshiyer/jina-ai-mcp-multimodal-search.git

See installation options