"How to create tasks or issues in Jira" matching MCP servers:

Enables MCP clients to open websites, capture screenshots at multiple breakpoints, and run automated design audits for issues like overflow, small tap targets, low contrast, missing alt text, tiny text, and clipped content.

A Model Context Protocol server for integrating JIRA with Claude, enabling AI assistants to create, search, update, and link JIRA tickets, as well as manage Zephyr test steps through natural language.

Enables AI agents and MCP clients to drive real Chrome, Edge, Brave, and Firefox browsers through native automation protocols. It exposes tools for navigation, page snapshots, form filling, clicks, screenshots, console/network inspection, and tab management.

Enables MCP clients to work with self-hosted Jira Server / Data Center 8.x through entity-based core tools, JSON-declared plugin tools, and bundled Zephyr Scale. It supports issue, comment, worklog, attachment, project, user, link, watcher, agile, and test management operations using Basic Auth or PAT with runtime field discovery.

Enables AI agents to inspect UI screen recordings as token-efficient image sheets with changed regions boxed, single-frame flicker flagged, and speech aligned to frames. Exposes watch_video and zoom_video tools so agents can pull dense keyframes across any time range or one sharp full-resolution frame, all processed locally.

MCP server for Jira Cloud tailored for ALM projects with Zephyr Scale integration, enabling issue management, sprint tracking, and test step operations.

Provides JIRA issue management and automation through MCP tools and standalone scripts, enabling reading, searching, analyzing, and modifying issues with batch processing and OCR capabilities.

Enables AI assistants to interact with a self-hosted Jira clone for testing, supporting project and issue management, comments, and search via MCP tools.

Enables intelligent analysis of regression test failures and automatic discovery of solutions in JIRA. Analyzes test logs using AI-driven algorithms and matches errors with relevant JIRA issues through natural language interactions.

Enables to interact with Jira AIO Test Case Management System, allowing retrieval of test cases, projects, folders, and search functionality.

Enables turning requirements documents into RFCs, functional test scenarios, and a traceability matrix, with tools to parse, diff, and verify coverage.

Enables comprehensive test management with JIRA's Zephyr system, allowing users to create and manage test plans, execute test cycles, track progress, and generate reports through natural language.

Automates the conversion of Figma designs into TypeScript React components and integrates with GitHub to create pull requests for the generated code. It includes visual regression testing with Playwright and accessibility validation to ensure implementations match the original designs.

Enables access to Xray Cloud test management on Jira Cloud through its REST v2 and GraphQL APIs, with configurable read/write toolsets, single-tool selection, and read-only or per-request credential modes.

MCP server to interact with the QMetry for Jira API, enabling management of test cases, test cycles, test plans, and more through well-defined tools.

Connects to Jira with Xray Test Management to extract test cases related to tickets and list test executions.

Enables agents to turn a plain-language description of a device into a complete, validated build package — parts list, 3D-printable parts, wiring, firmware code, picture assembly guide, and tests — that anyone can assemble without electronics experience.

Provides a testable gateway path for running a local MCP-style server in hosted-like conditions, with tenant-isolated reads, approval-denied writes, deterministic failures, and smoke-test verification.

Free JSON Schema validation, JSON to TypeScript, CSV to JSON, JSON repair and JSON to CSV for AI agents. Hosted MCP plus local STDIO. Connect once with a scoped key; enforce tool allowlists and quotas, inspect request IDs. Runnable repair → validate → CSV workflow. Bounded OpenAPI and webhook checks. No LLM calls or payments. Setup: invokeport.com/agents.

Lets an assistant read recorded production AI agent runs — steps, model and tool calls with arguments, and outcomes — along with the findings of comparison runs that checked a prompt or model change against those recordings, and verify a signed evidence bundle offline with no account or network. It is strictly read-only: no tool starts a replay or comparison, so nothing can spend money or act without a person deciding.