"Information about Jira Integration" matching MCP servers:

Enables MCP-capable AI assistants to search and retrieve past Jira issues, resolutions, and recurring topics from a fully local embedded knowledge base using hybrid semantic and keyword search.

MCP server for NodeBB forums that enables AI assistants to access forum data, search topics, and perform semantic search via the Model Context Protocol.

he Maximo MCP Server bridges AI agents (Claude, Cursor, Antigravity) with your IBM Maximo environment. It empowers your AI to autonomously discover Object Structures (APIs), inspect schemas, and query live data via OSLC. Instead of hallucinating field names, the AI validates queries instantly and ca

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for Jira Service Management (beta): https://www.cdata.com/download/download.aspx?sku=GKZK-V&type=beta

Enables integration with financial transaction data through REST APIs, PostgreSQL databases, and document storage systems. Demonstrates agentic AI capabilities by connecting to Alpha Vantage API and managing financial data through natural language interactions.

Production-ready MCP server that integrates OpenAI API with extensible tool support, enabling dynamic plugin loading and knowledge search capabilities through multiple interfaces including CLI and browser UI.

Enables scoped project discovery, deterministic rule and profile management, and durable automation with trigger/pause/resume/cancel and audit history for approved project contexts.

Enables chat with multiple LLM providers (OpenAI and Anthropic) while maintaining persistent conversation memory. Provides extensible tool framework for various operations including echo functionality and conversation storage/retrieval.

A FastMCP-based MCP server for the R2R API, enabling integration with document management, knowledge graphs, and RAG systems through automatically generated tools and resources.

Routes requests to multiple downstream Model Context Protocol servers and provides a LangGraph.js-based agent with RAG capabilities for development assistance.

Enables MCP clients to connect to LLM/API services using the Model Context Protocol, providing real-time interaction and tool access. Also offers RESTful API endpoints via FastAPI for programmatic integration with existing systems.

Provides file system access for resume matching through JSON-RPC, enabling semantic search and workflow orchestration with LangGraph.

Implements a Model Control Protocol server integrated with Google Gemini LLM, providing a flexible framework for building AI-powered applications.

An MCP-compatible Flask server that integrates with the educhain Python library to dynamically generate educational content for Claude Desktop, including multiple-choice questions, lesson plans, and flashcards.

A secure Model Context Protocol implementation that enables AI agents to query PostgreSQL databases through predefined tools for employee, project, and issue data. It protects sensitive credentials and prevents arbitrary SQL execution by acting as a controlled connector layer between the LLM and the database.

Enables Claude Desktop to trigger and interact with Make.com automation scenarios through webhooks. Allows users to execute complex workflows and integrations with third-party services like Google Sheets, Notion, and Slack using natural language commands.

MCP server that allows AI agents to read, search, summarize, and answer questions about local PDF, DOCX, XLSX, and TXT files through five tools.

Enables AI assistants to search and ask questions about documents stored in AnythingLLM workspaces through MCP tools.

Enables AI assistants to search and retrieve transcripts from the Entra.Chat podcast about Microsoft Entra ID, with timestamped YouTube links and guest information.

DevsContext is an MCP server that provides AI coding agents with synthesized engineering context—requirements, decisions, architecture, and standards—from tools like Jira and Slack. It fetches and synthesizes relevant information on demand to help agents work on tasks correctly.

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

Cline MCP integration that allows users to save, search, and format memories with semantic understanding, providing tools to store and retrieve information using vector embeddings for meaning-based search.

A Model Context Protocol server that allows AI agents to perform WHOIS lookups, enabling users to directly ask the AI about domain availability, ownership, registration details, and other domain information.

Provides LLMs with specialized knowledge about fountain pen inks, enabling intelligent ink search, color matching, and recommendations through various tools.

A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.

Provides personalized Data Engineering learning updates by fetching recent news about DE concepts, patterns, and technologies, while tracking user knowledge to present only new information relevant to their learning journey.

Enables web search capabilities through the Tavily API, allowing users to search the internet for information using natural language queries. Demonstrates MCP server implementation with external API integration.

A server that connects MCP clients (Claude, Cursor, Windsurf, etc.) to EventCatalog instances, allowing users to ask questions about their event-driven architecture and get insights about domains, services, and message schemas directly within their development tools.

A Model Context Protocol server that provides natural language access to Cortex API, enabling users to query information about their system's structure and make faster decisions about services, incidents, and team responsibilities.

Enables AI agents to query up-to-date, sourced information about workflow automation services, AI agents, integrations, pricing, case studies, FAQs, and contact details in Dutch and English through read-only tools.

Exposes a personalized AI agent that reads your resume and provides intelligent responses about your professional background through a standardized MCP server interface with RAG capabilities.

Enables AI assistants to search internal knowledge bases, create Confluence articles, file Jira tickets, and look up Okta users, with guardrails and streaming support.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.

Provides a local vector memory store for AI agents with semantic search, offline embeddings, and MCP integration, enabling tools like Claude and Cursor to store and retrieve information without cloud dependencies.

MCP server for local-first RAG over Obsidian vaults, enabling AI agents to search and ask questions about notes with grounded citations.

CTX is a tool made to solve a big problem when chatting with LLMs like ChatGPT or Claude: giving them enough context about your project.

Enables users to query information about LPDP scholarship financial disbursement using RAG with Pinecone vector search and Gemini 2.0 Flash, answering questions about funding components, deadlines, living allowances, and required documents.

Enables AI assistants to query and analyze databases using natural language through MCP and HTTP API, supporting 17+ databases and integration with 50+ platforms.

A Model Context Protocol (MCP) server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.