Jina AI (Search Foundation & LLM Grounding)

Jina AI (Search Foundation & LLM Grounding) MCP Connector for Claude

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Power your RAG and search via Jina AI — generate embeddings, rerank documents, read URLs, and perform semantic web search.

6 tools Official Updated Jun 28, 2026 Official Vinkius Partner

Connect your Jina AI account to any AI agent and take full control of state-of-the-art search infrastructure and LLM grounding through natural conversation.

What you can do

  • LLM Grounding & Reader — Extract clean, readable Markdown context from any web URL, stripping away noise and navigation to feed high-quality data to your agent
  • Semantic Web Search — Perform context-rich web searches that return structured results specifically optimized for RAG pipelines and AI analysis
  • Vector Embeddings — Generate high-quality embeddings using Jina's advanced models to power semantic search and document similarity workflows
  • Precision Reranking — Improve search relevance by re-ordering candidate documents based on their semantic match to a specific query block
  • Zero-Shot Classification — Categorize text inputs against custom labels with confidence scores without training specific models manually
  • Intelligent Segmentation — Break down long documents into semantically cohesive chunks to optimize retrieval-augmented generation (RAG)

How it works

  1. Subscribe to this server
  2. Enter your Jina AI API Key
  3. Start grounding your AI with real-time web intelligence from Claude, Cursor, or any MCP-compatible client

Who is this for?

  • AI & RAG Developers — provide your agents with real-time web access and precise document retrieval capabilities through natural conversation
  • Data Scientists — test embedding models and reranking logic without writing manual Python scripts or using curl
  • Automation Engineers — automate the extraction of clean web content and semantic classification for large-scale data pipelines
embeddingsragsemantic-searchweb-scrapingllm-groundingdata-extraction

6 tools expose this connector's capabilities to your AI agent.

generate_embeddings

The input must be a JSON array of strings. Generate vector embeddings from text

rerank_documents

Rerank search documents against a query

read_url_content

Excellent for grounding LLMs with live web content. Read and extract clean text from a URL

search_web_jina

Returns context-rich structured search results, suitable for RAG pipelines. Perform a semantic web search

classify_texts

Perform zero-shot text classification

segment_content

Semantically segment and chunk long text content

See how to talk to your AI agent using Jina AI (Search Foundation & LLM Grounding).

Extract the main content from 'https://jina.ai/embeddings' as Markdown

Reading URL… I've extracted the clean text from the page. It covers Jina's embedding models (v2 and v3), multilingual support for 80+ languages, and common use cases like semantic search and recommendation systems. Would you like a summary?

Search the web for the latest updates on 'DeepSeek-V3 architecture'

Searching the web… Based on the structured results from Jina Search, DeepSeek-V3 features a Multi-head Latent Attention (MLA) mechanism and a Mixture-of-Experts (MoE) architecture with 671B total parameters. Would you like me to read the full technical paper URL?

Segment this long text into semantically cohesive chunks: [text content]

Segmenting content… I've broken your text into 5 semantically distinct segments. Each segment focuses on a single core topic (e.g., Introduction, Architecture, Benchmarks). This is now optimized for vector storage and RAG retrieval.

Use the `read_url_content` tool to give your agent access to live web data. By converting URLs into clean Markdown, your agent can 'read' the latest information from documentation or news sites, grounding its answers in up-to-date facts.

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