"Permission requirements to create and edit issues in Jira using MCP" matching MCP servers:

Enables secure file system operations (read, write, delete) and simulated command execution with server-enforced permission policies, risk assessment, and human-in-the-loop approval.

Enables triage of GitHub issues by fetching and filtering issues, checking claim status, and listing new open issues across a watch-list.

Enables management of software requirements in compliance with IEC 62304 Class C medical device standards, including requirement creation, versioning, traceability matrix generation, and risk control integration.

An async MCP server for JIRA integration, enabling AI assistants to search, create, and manage JIRA issues via JQL and other operations.

Lets agents read a GitHub repository's live issues through read-only tools for listing, filtering, fetching details with comments and cross-references, searching, and exploring labels and milestones, with transparent pagination and provenance notes.

Read, create, and modify Figma designs programmatically via MCP, empowering AI to execute Figma changes safer, cleaner, and faster.

Enables MCP clients to read and create Jira issues against Jira Cloud or Data Center through a self-hosted FastMCP server with secure authentication and configurable fields.

Enables AI agents to programmatically edit videos via FFmpeg, providing tools for probing, trimming, concatenating, timeline composition, LUT generation/application, rendering, and edit verification over MCP.

A minimal MCP server using FastMCP to demonstrate tool creation, including a get_weather example.

Enables AI assistants to search, validate, and edit OpenStreetMap data through natural language commands and built-in safety protections. It supports discovery of nearby amenities, geographic data exploration, and secure map editing via OAuth authentication.

Enables text-guided image editing with 1–9 reference images, returning generated image URLs via Alibaba Cloud's Wan2.7 model.

Enables coding agents to read Jira Cloud issues, search via JQL, and retrieve comments, attachments, and change history through a lightweight, context-efficient MCP toolset.

Provides read-only access to Jira, enabling users to retrieve issue details, comment threads, linked-issue context, epic children, and JQL search results as compact Markdown.

Exposes Jira task management (list, create with two-step confirmation, start) to AI agents while keeping credentials hidden, and deliberately does not handle git operations.

MCP server for Jira Cloud enabling issue tracking, comments, transitions, and attachments management through natural language.

MCP server for Jira Server/Data Center that enables issue retrieval, JQL search, comment posting, and Tempo worklog queries via the atlassian-python-api.

Enables querying and managing Jira issues and Confluence pages through their Cloud REST APIs, preserving markdown formatting via direct ADF conversion.

Provides AI assistants with tools to query, filter, and analyze JIRA issues stored in Snowflake, enabling natural language interaction with issue data.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

Enables interaction with Jira through OAuth 2.0 to create and manage issues directly from MCP-compatible clients. It supports secure authentication and local token caching to facilitate automated task management within Jira projects.