"Resources for learning about data analysis" matching MCP servers:

A task manager MCP server that demonstrates core MCP concepts (resources, tools, prompts, stdio) and syncs data with a Next.js web app.

A powerful Model Context Protocol (MCP) server for seamless Jira integration with AI assistants like Claude. Features advanced search capabilities, intelligent pod aliases, comprehensive ticket analysis, and full API support.

Provides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.

MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.

Enables AI-powered project management by connecting Claude to Planka kanban boards via 75+ MCP tools for creating, updating, and managing projects, boards, lists, and cards.

Enables AI assistants to deeply introspect Jira Data Center configurations — including workflows, schemes, automation, and Assets — through 76 read-only tools, without any modification capability.

A Model Context Protocol (MCP) server that connects AI assistants to Jira Server/Data Center, enabling issue management, JQL search, sprint tracking, and natural language interaction with Jira.

Enables voice-driven task management by letting users view, select, complete, and reschedule tasks through natural-language commands and a native confirmation card with date parsing and batch handling.

A bridge between MCP Host applications and mem0 cloud service, specialized for project management with capabilities to store, retrieve, and search project information within a structured format.

Enables AI agents to read-only inspect an OmniFocus database, retrieving exact tasks and projects, Inbox roots, complete project hierarchies, tags, dates, notes and workload or review overviews. All access is read-only and bounded, with no mutation, write tools or sync triggers.

A Model Context Protocol server that provides seamless integration with Coroot observability platform, enabling monitoring of applications, analysis of performance metrics, examination of logs and traces, and management of infrastructure through Coroot's comprehensive API.

A Python MCP server that retrieves requirements and bug data from TAPD platform to provide AI clients with project management information.

Enables interaction with Azure DevOps through natural language in Cursor IDE. Supports work item management, pull requests, builds, releases, test management, and guided workflows for development teams, QA testers, and release management.

Exposes internal employee directories and project management systems to AI models through standardized tools and resources. It enables AI assistants to search for team members, query project statuses, and explore organizational hierarchies with secure role-based access control.

Enables the analysis of GitHub Pull Requests to extract metadata, commits, and code changes for structured AI insights. It also supports the optional creation of Notion pages to store and organize the resulting analysis.

Enables creating Jira issues and listing issue types for a Jira Cloud project using Basic Auth credentials.

MCP server for Everhour time tracking with first-class Asana integration, enabling tasks lookup, time logging, timer control, and timesheet review.

Exposes the WCC event, mentorship, and analytics pipeline tools as MCP endpoints with per-group authentication and dry-run support, enabling agents to manage events, mentors, and analytics.

Enables employees, approvers, and admins to register and manage time, view balances, cases, employees, and planning through Visma Intempus, with per-user scoping and fail-closed identity.

MCP server that connects to Jira Data Center, enabling issue search, retrieval, creation, comments, workflow transitions, project listing, arbitrary JQL, and turning meeting notes into structured Jira stories, tasks, and bugs.