Yandex Tracker▌
by aikts
Integrate Yandex Tracker with project management software for advanced issue tracking, project plan templates, Gantt cha
Integrates with Yandex Tracker for comprehensive issue management, queue operations, user management, and project data access with OAuth 2.0 authentication and Redis caching support.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Teams using Yandex Tracker for project management
- / Automating issue tracking workflows
- / AI assistants helping with ticket management
capabilities
- / Create and update Yandex Tracker issues
- / Manage queues with pagination and metadata
- / Execute status transitions and workflow changes
- / Search issues using Yandex Tracker Query Language
- / Retrieve user account and organizational data
- / Access custom fields and project configurations
what it does
Connects AI assistants to Yandex Tracker for managing issues, queues, users, and projects. Provides full access to Yandex Tracker's API with OAuth authentication and optional Redis caching.
about
Yandex Tracker is a community-built MCP server published by aikts that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate Yandex Tracker with project management software for advanced issue tracking, project plan templates, Gantt cha
how to install
You can install Yandex Tracker in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
Apache-2.0
Yandex Tracker is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
Yandex Tracker MCP Server
mcp-name: io.github.aikts/yandex-tracker-mcp
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with Yandex Tracker APIs. This server provides secure, authenticated access to Yandex Tracker issues, queues, comments, worklogs, and search functionality with optional Redis caching for improved performance.
<a href="https://glama.ai/mcp/servers/@aikts/yandex-tracker-mcp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@aikts/yandex-tracker-mcp/badge" /> </a>Documentation in Russian is available here / Документация на русском языке доступна здесь.
Features
- Complete Queue Management: List and access all available Yandex Tracker queues with pagination support, tag retrieval, and detailed metadata
- User Management: Retrieve user account information, including login details, email addresses, license status, and organizational data
- Full Issue Lifecycle: Create, read, update, and manage issues with support for custom fields, attachments, and workflow transitions
- Status Workflow Management: Execute status transitions, close issues with resolutions, and navigate complex workflows
- Field Management: Access global fields, queue-specific local fields, statuses, issue types, priorities, and resolutions
- Advanced Query Language: Full Yandex Tracker Query Language support with complex filtering, sorting, and date functions
- Performance Caching: Optional Redis caching layer for improved response times
- Security Controls: Configurable queue access restrictions and secure token handling
- Multiple Transport Options: Support for stdio, SSE (deprecated), and HTTP transports for flexible integration
- OAuth 2.0 Authentication: Dynamic token-based authentication with automatic refresh support as an alternative to static API tokens
- Organization Support: Compatible with both standard and cloud organization IDs
Organization ID Configuration
Choose one of the following based on your Yandex organization type:
- Yandex Cloud Organization: Use
TRACKER_CLOUD_ORG_IDenv var later for Yandex Cloud-managed organizations - Yandex 360 Organization: Use
TRACKER_ORG_IDenv var later for Yandex 360 organizations
You can find your organization ID in the Yandex Tracker URL or organization settings.
MCP Client Configuration
Installing extension in Claude Desktop
Yandex Tracker MCP Server can be one-click installed in Claude Desktop as and extension.
Installation
- Download the
*.mcpbfile from GitHub Releases. - Double-click the downloaded file to install it in Claude Desktop.

- Provide your Yandex Tracker OAuth token when prompted.

- Make sure extension is enabled - now you may use this MCP Server.
Manual installation
Prerequisites
- uv installed globally
- Valid Yandex Tracker API token with appropriate permissions
The following sections show how to configure the MCP server for different AI clients. You can use either uvx yandex-tracker-mcp@latest or the Docker image ghcr.io/aikts/yandex-tracker-mcp:latest. Both require these environment variables:
- Authentication (one of the following):
TRACKER_TOKEN- Your Yandex Tracker OAuth tokenTRACKER_IAM_TOKEN- Your IAM tokenTRACKER_SA_KEY_ID,TRACKER_SA_SERVICE_ACCOUNT_ID,TRACKER_SA_PRIVATE_KEY- Service account credentials
TRACKER_CLOUD_ORG_IDorTRACKER_ORG_ID- Your Yandex Cloud (or Yandex 360) organization ID
Configuration file path:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
Using uvx:
{
"mcpServers": {
"yandex-tracker": {
"command": "uvx",
"args": ["yandex-tracker-mcp@latest"],
"env": {
"TRACKER_TOKEN": "your_tracker_token_here",
"TRACKER_CLOUD_ORG_ID": "your_cloud_org_id_here",
"TRACKER_ORG_ID": "your_org_id_here"
}
}
}
}
Using Docker:
{
"mcpServers": {
"yandex-tracker": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"-e", "TRACKER_TOKEN",
"-e", "TRACKER_CLOUD_ORG_ID",
"-e", "TRACKER_ORG_ID",
"ghcr.io/aikts/yandex-tracker-mcp:latest"
],
"env": {
"TRACKER_TOKEN": "your_tracker_token_here",
"TRACKER_CLOUD_ORG_ID": "your_cloud_org_id_here",
"TRACKER_ORG_ID": "your_org_id_here"
}
}
}
}
</details>
<details>
<summary><strong>Claude Code</strong></summary>
Using uvx:
claude mcp add yandex-tracker uvx yandex-tracker-mcp@latest \
-e TRACKER_TOKEN=your_tracker_token_here \
-e TRACKER_CLOUD_ORG_ID=your_cloud_org_id_here \
-e TRACKER_ORG_ID=your_org_id_here \
-e TRANSPORT=stdio
Using Docker:
claude mcp add yandex-tracker docker "run --rm -i -e TRACKER_TOKEN=your_tracker_token_here -e TRACKER_CLOUD_ORG_ID=your_cloud_org_id_here -e TRACKER_ORG_ID=your_org_id_here -e TRANSPORT=stdio ghcr.io/aikts/yandex-tracker-mcp:latest"
</details>
<details>
<summary><strong>Cursor</strong></summary>
Configuration file path:
- Project-specific:
.cursor/mcp.jsonin your project directory - Global:
~/.cursor/mcp.json
Using uvx:
{
"mcpServers": {
"yandex-tracker": {
"command": "uvx",
"args": ["yandex-tracker-mcp@latest"],
"env": {
"TRACKER_TOKEN": "your_tracker_token_here",
"TRACKER_CLOUD_ORG_ID": "your_cloud_org_id_here",
"TRACKER_ORG_ID": "your_org_id_here"
}
}
}
}
Using Docker:
{
"mcpServers": {
"yandex-tracker": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"-e", "TRACKER_TOKEN",
"-e", "TRACKER_CLOUD_ORG_ID",
"-e", "TRACKER_ORG_ID",
"ghcr.io/aikts/yandex-tracker-mcp:latest"
],
"env": {
"TRACKER_TOKEN": "your_tracker_token_here",
"TRACKER_CLOUD_ORG_ID": "your_cloud_org_id_here",
"TRACKER_ORG_ID": "your_org_id_here"
}
}
}
}
</details>
<details>
<summary><strong>Windsurf</strong></summary>
Configuration file path:
~/.codeium/windsurf/mcp_config.json
Access via: Windsurf Settings → Cascade tab → Model Context Protocol (MCP) Servers → "View raw config"
Using uvx:
{
"mcpServers": {
"yandex-tracker": {
"command": "uvx",
"args": ["yandex-tracker-mcp@latest"],
"env": {
"TRACKER_TOKEN": "your_tracker_token_here",
"TRACKER_CLOUD_ORG_ID": "your_cloud_org_id_here",
"TRACKER_ORG_ID": "your_org_id_here"
}
}
}
}
Using Docker:
{
"mcpServers": {
"yandex-tracker": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"-e", "TRACKER_TOKEN",
"-e", "TRACKER_CLOUD_ORG_ID",
"-e", "TRACKER_ORG_ID",
"ghcr.io/aikts/yandex-tracker-mcp:latest"
],
"env": {
"TRACKER_TOKEN": "your_tracker_token_here",
"TRACKER_CLOUD_ORG_ID": "your_cloud_org_id_here",
"TRACKER_ORG_ID": "your_org_id_here"
}
}
}
}
</details>
<details>
<summary><strong>Zed</strong></summary>
Configuration file path:
~/.config/zed/settings.json
Access via: Cmd+, (macOS) or Ctrl+, (Linux/Windows) or command palette: "zed: open settings"
Note: Requires Zed Preview version for MCP support.
Using uvx:
{
"context_servers": {
"yandex-tracker": {
"source": "custom",
"command": {
"path": "uvx",
"args": ["yandex-tracker-mcp@latest"],
"env": {
"TRACKER_TOKEN": "your_tracker_token_here",
"TRACKER_CLOUD_ORG_ID": "your_cloud_org_id_here",
"TRACKER_ORG_ID": "your_org_id_here"
}
}
}
}
}
Using Docker:
{
"context_servers": {
"yandex-tracker": {
"source": "custom",
"command": {
"path": "docker",
"args": [
"run", "--rm", "-i",
"-e", "TRACKER_TOKEN",
"-e", "TRACKER_CLOUD_ORG_ID",
"-e", "TRACKER_ORG_ID",
"ghcr.io/aikts/yandex-tracker-mcp:latest"
],
"env": {
"TRACKER_TOKEN": "your_tracker_token_here",
"TRACKER_CLOUD_ORG_ID": "your_cloud_org_id_here",
"TRACKER_ORG_ID": "your_org_id_here"
}
}
}
}
}
</details>
<details>
<summary><strong>GitHub Copilot (VS Code)</strong></summary>
Configuration file path:
- Workspace:
.vscode/mcp.jsonin your project directory - Global: VS Code
settings.json
Option 1: Workspace Configuration (Recommended for security)
Create .vscode/mcp.json:
Using uvx:
{
"inputs": [
{
"type": "promptString",
"id": "tracker-token",
"description": "Yandex Tracker Token",
"password": true
},
{
"type": "promptString",
"id": "cloud-org-id",
"description": "Yandex Cloud Organization ID"
},
{
"type": "promptString",
"id": "org-id",
"description": "Yandex Tracker Organization ID (optional)"
}
],
"servers": {
"yandex-tracker": {
"type": "stdio",
"command": "uvx",
"args": ["yandex-tracker-mcp@latest"],
"env": {
"TRA
---
FAQ
- What is the Yandex Tracker MCP server?
- Yandex Tracker is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Yandex Tracker?
- This profile displays 74 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.5★★★★★74 reviews- ★★★★★Shikha Mishra· Dec 28, 2024
According to our notes, Yandex Tracker benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Yusuf Huang· Dec 24, 2024
Yandex Tracker has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Zaid Jain· Dec 20, 2024
According to our notes, Yandex Tracker benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Noor Diallo· Dec 16, 2024
Useful MCP listing: Yandex Tracker is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Kwame Dixit· Dec 12, 2024
Yandex Tracker has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Yusuf Thomas· Dec 8, 2024
Yandex Tracker is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Liam Yang· Dec 8, 2024
According to our notes, Yandex Tracker benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Sofia Sethi· Nov 27, 2024
Yandex Tracker has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Sofia Harris· Nov 27, 2024
We wired Yandex Tracker into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Yash Thakker· Nov 19, 2024
We wired Yandex Tracker into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
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