"Qase" matching MCP servers:

A local knowledge base system based on ChromaDB that supports automatic chunking, vector storage, and efficient similarity retrieval of txt and pdf documents, with MCP protocol support allowing AI assistants to directly access knowledge management functions.

A generic Model Context Protocol framework for building AI-powered applications that provides standardized ways to create MCP servers and clients for integrating LLMs with support for Ollama and Supabase.

Enables semantic search across text documents using vector embeddings stored in PostgreSQL. Provides multiple search modalities including semantic similarity, question/answer, and style-based search through a retrieval-augmented generation system.

A retrieval-only MCP server that searches a vectorized Wangsu Terraform Provider documentation knowledge base and returns relevant snippets with rules to guide the caller's LLM in generating accurate, provider-specific answers. It offloads all inference to the calling client, as it does not invoke any LLM or require API keys on the server side.

Enables indexing a Markdown knowledge base with local sentence embeddings and serving retrieval over MCP, allowing LLM clients to query the indexed knowledge.

A conversational chat interface that uses RAG over a synthetic test corpus and a domain-knowledge corpus, exposing retrieval via MCP.

Enables semantic search and document management with support for text, PDF, and image uploads using your own Supabase database and OpenAI API keys. Supports multi-tenant deployment on Cloudflare Workers or local hosting.

Read-only MCP server with hybrid search combining dense semantic and sparse keyword retrieval via Qdrant, enabling document querying and fetching for ChatGPT Deep Research.