Search across screen, voice, and clipboard entries to find content semantically related to any query. Returns a unified ranked list with source tags for open-ended recall spanning multiple data types.
AGPL 3.0
712,405 tools. Updated 2026-10-28 16:25
"Searching for technologies and tools related to embeddings and vector databases" matching MCP tools:
- Filter and retrieve Manticore Search documentation files using regex patterns to efficiently find multiple topics in a single call.Apache 2.0
- Initialize a new vector collection at a specified path, define its vector and scalar fields with optional indexes, and open it for use.Apache 2.0
- Retrieve the top-K most similar vertices to a query vector using vector similarity search. Provide vertex type, vector attribute, and query vector to get ranked results with distance scores.Apache 2.0
- Bulk-load vector data from a CSV file into a vertex type's vector attribute by creating and running a GSQL loading job.Apache 2.0
- Generate embeddings for text and images into one shared 768-dim vector space for cross-modal similarity, search, and dot-product comparison.MIT
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- Create typed tables with full-text BM25 indexes and optional vector embeddings, enabling keyword, semantic, and hybrid search over stored data.Apache 2.0
- Retrieve sessions related to a given session using keyword co-occurrence and semantic embeddings.Business Source 1.1
- Find documents similar in meaning to a given record using vector search by entity and ID, returning the closest matches (10 by default).MIT
- Discover available API toolsets with descriptions, tool counts, and examples to identify relevant tools before searching.MIT
- Search Veeam port requirements using natural language queries. Understands synonyms and related concepts to find matches even without exact keywords.MIT
- Save web pages or raw notes into a persistent memory vault using automated text extraction, OpenGraph metadata parsing, and vector embeddings for reliable semantic recall.MIT
- Generate 3072-dimensional semantic vector embeddings from text to power RAG retrieval and agent memory. Use Gemini Embedding 2 for accurate similarity search.MIT
- Converts plain text documents into dense vector embeddings and upserts them into a Zvec collection for storage.Apache 2.0
- Retrieve vector attribute name, dimension, and metric for vertex types, with optional filtering by vertex type.Apache 2.0
- Find similar vector embeddings in Zilliz Cloud collections using vector similarity search with optional filtering and result customization.Apache 2.0
- Retrieve all fields and their data types from a specified Fibery database and its related databases to understand data structure.MIT
- Search vector databases by combining semantic similarity with structured filters, then refine results using ranking strategies to retrieve relevant data.Apache 2.0
- Retrieve technographic tags to filter businesses by web technologies like CMS or analytics tools. Optionally narrow results with a keyword.MIT
- Retrieve memories related to an anchor memory, categorized as forward, backward, or lateral relationships based on vector similarity.MIT
- Find semantically similar thoughts to a text query via AI embeddings. Adjust result count and minimum similarity score to refine matches.MIT