"How to Create a 3D Model Using Blender" matching MCP servers:

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Model DBMS. ArcadeDB supports Vector Embeddings.

A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.

Semantic skill-library search MCP server that finds relevant skills or Markdown knowledge files from local directories using embeddings, SQLite vector storage, and Reciprocal Rank Fusion.

A conversational application server that integrates LLM capabilities via Ollama with vector memory context, supporting multiple users, sessions, automatic history summarization, and a plugin system for executing real actions.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.

An MCP server that enables searching and retrieving ACL NLP conference papers from a Qdrant vector database using semantic search and structured filters like year, venue, and field of study.

Enables AI agents to interact with an embedded graph database (GrafeoDB) via the Model Context Protocol, providing tools for graph CRUD, GQL queries, full-text and vector search, and graph algorithms.

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.

An intelligent memory MCP server that provides AI applications with semantic search, entity extraction, and knowledge graph capabilities using local Redis caching and optional cloud sync. It enables LLMs to store and retrieve long-term context across sessions with high-performance multi-tier storage.

Provides semantic search over AWS Cloudscape Design System documentation using natural language queries, enabling AI assistants to efficiently find and retrieve component documentation with token-efficient responses.

Enables semantic search and analysis of customer support tickets. Provides tools to search tickets, analyze the dataset, and retrieve individual tickets using natural language.

A local-first personal RAG memory system that turns AI conversation history into a searchable, retrievable knowledge base via MCP, enabling LLMs to semantically search past conversations.

Enables LLM hosts to retrieve live, relevant documentation excerpts from official library docs sites via a search-and-RAG tool, avoiding reliance on training data.

Gives AI agents persistent, local-first memory using SQLite and on-device embeddings, enabling semantic search and recall across sessions with no cloud calls.

askDB is an MCP server that retrieves relevant database schema (DDL) from a Pinecone index and provides it to LLMs to write SQL, without connecting to the database itself.

Provides SQL-backed semantic search over indexed notes using pgvector, exposing tools to search and list note sources via natural language.