"A search for Yandex, the Russian multinational technology company" matching MCP servers:

Enables MCP-compatible assistants to connect to a user's workspace, read across boards, notes, checklists, habits, money, meals, chores, and chat, and make scoped additions, changes, or removals with attribution.

MCP server for Yandex Tracker API, enabling AI assistants to search, read, create, and edit issues, as well as manage comments, attachments, and links in Yandex Tracker.

Enables LLM agents to interact with Yandex Tracker (and future Yandex services) using per-user credentials instead of a shared org-wide token, supporting both single-user and multi-user modes.

Enables AI assistants to securely interact with Yandex Tracker, managing issues, queues, comments, worklogs, and search, including attaching files to issues.

This MCP enables access to Yandex Tracker issue management. Currently it supports all read operations to retrieve queues, issues and users from Yandex Tracker.

A Model Context Protocol server that enables AI assistants to securely interact with Yandex Tracker, supporting issues, queues, comments, worklogs, search, and workflow management with optional Redis caching.

Enables AI assistants to read and manage issues, comments, checklists, worklogs, queues, users, metadata, and time reports through the Yandex Tracker REST API v3, requiring user confirmation before data-changing operations.

A Node.js MCP server that enables AI assistants to interact with Yandex Tracker task management system through a standardized protocol, supporting operations like creating, updating, and searching issues.

Enables people to open a shared group chat as a card inside their own AI chat, where they and their AIs talk together and post, claim, and confirm tasks on a self-organizing board. Its tools also let any AI read the repository's code and submit a proposed change that becomes a pull request.

Enables AI assistants to interact with Yandex.Tracker task management system through MCP protocol. Supports creating and managing issues, searching tasks, handling comments, managing projects and queues, and generating analytics reports.

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.

Exposes Yandex Tracker's REST API v3 as tools for LLM clients, enabling issue management and queue operations via natural language.

Enables search and management of Azure DevOps work items and wiki pages via MCP, wrapping the ado-search CLI.

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 an MCP-capable coding agent to run a local-first job search: recording verified opportunities in a private SQLite tracker, tracking follow-ups and outcomes, and preparing evidence-based resumes, application answers, and outreach drafts. It keeps all personal data on the user's machine and stops before submitting applications, uploading documents, or contacting anyone.

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.

Enables AI agents to manage Yandex Tracker tasks with tools for creating, reading, updating, and transitioning issues via a Streamable HTTP endpoint.

Enables managing employee information, tasks (todos), and company policies via MCP tools and resources.

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.