Generate vector embeddings from text for semantic search, RAG, clustering, or similarity tasks. Choose between query or document input type and adjust model quality and dimensionality.
MIT
167,424 tools. Last updated 2026-06-02 20:54
"Understanding Document Embeddings, Knowledge Graphs, and Vector Representations" matching MCP tools:
- Find documents semantically related to any file or connector document. Uses vector embeddings to perform similarity search and return the most relevant content.MIT
- Find similar vector embeddings in Zilliz Cloud collections using vector similarity search with optional filtering and result customization.Apache 2.0
- Delete a knowledge document from Anam AI using its unique ID. Removes the specified document from your knowledge base.MIT
- Preview what will be removed (chunks, images) from the RAG knowledge base, then confirm to delete the document and all associated data.MIT
- Create and manage per-team knowledge bases with vector-indexed documents. Agents search via hybrid cosine similarity and keyword fallback at runtime.AGPL 3.0
Matching MCP Servers
- Alicense-qualityCmaintenanceA secure vector-based memory server that provides persistent semantic memory for AI assistants using sqlite-vec and sentence-transformers. It enables semantic search and organization of coding experiences, solutions, and knowledge with features like auto-cleanup and deduplication.Last updatedMIT
- Alicense-qualityBmaintenanceAn MCP server that enables AI agents to query specialized, domain-specific knowledge bases built using the LightRAG framework for enhanced retrieval-augmented generation. It allows for managing and searching knowledge graphs and vector embeddings to provide accurate, context-aware information during an AI assistant's reasoning process.Last updated52MIT
Matching MCP Connectors
The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.
AI reasoning checks any document against known international standards before your agent acts on it.
- Indexes a codebase to build vector embeddings for semantic code search across 50+ languages. Required initial step before searching code.MIT
- Add nodes to knowledge graphs for organizing components, events, requirements, or concepts. Supports multiple graph types including topology, timelines, and knowledge bases.MIT
- Create a vector store from S3 markdown files by downloading, chunking, embedding with AWS Bedrock Titan, and storing in PostgreSQL for semantic search.MIT
- Permanently delete a registered media asset by removing storage files, vector embeddings, and all associated metadata. This action cannot be undone.MIT
- Search Fodda's expert-curated knowledge graphs using hybrid vector and keyword methods to find relevant trends and articles across industries like retail, beauty, and sports.Unlicense - libtelnet variant
- Regenerate vector embeddings for all published content to maintain search accuracy and relevance in LightCMS. Use after initial setup or when embeddings become outdated.MIT
- Initialize a domain with a knowledge base by uploading markdown content. Each ## section becomes a searchable vector-embedded document. Run this before testing or serving chat queries.MIT
- Find entities similar to your query using vector embeddings and semantic search capabilities.MIT
- Check the current status of the knowledge base vector store to monitor build progress, document count, and cache statistics.MIT
- Generate numerical vector embeddings from text input to enable semantic search, clustering, or similarity comparisons.AGPL 3.0
- Analyze local codebases to extract code knowledge, signatures, and docstrings, and optionally generate API reference documentation and dependency graphs.MIT
- Activate vector search capabilities in Meilisearch to enable semantic similarity-based document retrieval using AI embeddings.MIT
- Remove a document from Chroma vector database using its unique ID. This tool ensures efficient document management and cleanup in the MCP server environment.MIT
- Find entities in your knowledge graph by semantic meaning using vector embeddings and similarity thresholds. Specify query, limit, similarity, entity types, and hybrid search options.MIT