"Managing and searching Jira ticket creation" matching MCP servers:

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.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

An MCP server that enables saving, retrieving, and managing research content using ChromaDB vector storage and semantic search.

A Model Context Protocol server for reading TiDB RAG knowledge base, enabling browsing, searching, and vector search of knowledge entries.

A ChromaDB-based document storage MCP server that enables adding, storing, and summarizing notes with a custom note:// URI scheme and a single tool for note creation.

A Model Context Protocol server that allows Large Language Models to interact with Astra DB databases, providing tools for managing collections and records through natural language commands.

An MCP server for OpenServerless that exposes action tools for creating, invoking, and managing API endpoints with integrated services like S3, PostgreSQL, Redis, and Milvus.

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

Enables AI agents to autonomously create and manage topic-specific vector knowledge bases with end-to-end functionality including project creation, content ingestion from URLs, semantic search, and progress tracking. Provides a complete research workflow without exposing low-level APIs.

A Node.js-based MCP server that enables AI agents to generate embeddings, index documents, and perform semantic vector searches using OpenAI and Chroma. It facilitates the creation of retrieval-augmented generation (RAG) pipelines for internal knowledge assistants and document-based workflows.

An MCP server for querying and managing LlamaIndex documents stored in Qdrant vector databases, with automatic embedding model detection and extensive tools for search, retrieval, and collection management.

MCP server that enables searching a TOTVS Protheus ERP knowledge base using vector similarity and reranking.

Enables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.

Enables vector similarity search and serving of Svelte documentation via the MCP protocol, with support for local caching and multiple llms.txt documentation formats.

Provides persistent memory and knowledge graph capabilities for AI assistants using local SQLite storage. Enables creating, searching, and managing entities, relationships, and observations with vector search support across conversations.

An MCP server that gives AI assistants persistent memory using an OpenAI vector store, enabling saving and semantic searching of text memories across sessions.

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.

A Model Context Protocol server for indexing and searching local document collections with hybrid BM25+vector search, optimized for Korean and English, supporting DOCX, PDF, audio, and more.

Enables LLMs to interact with Rememberizer Vector Store for semantic search, document creation, deletion, and modification.

An interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.