"Linear ticket integration tool for code context enhancement" matching MCP servers:

Multi-provider embedding MCP server with local Milvus for semantic code search. Works with Claude Code, Cursor, Windsurf, and other MCP-compatible IDEs.

Enables AI models to interact with Linear for issue tracking and project management through capabilities like creating/searching issues, managing sprints, and retrieving workflow states.

Automates parking ticket detection, evidence gathering, and dispute preparation across multiple US cities.

CTX is a tool made to solve a big problem when chatting with LLMs like ChatGPT or Claude: giving them enough context about your project.

Builds rich code graphs from TypeScript/NestJS codebases using AST analysis and Neo4j, enabling semantic search, natural language querying, and intelligent graph traversal to provide deep contextual understanding of code relationships and dependencies.

Provides intelligent code context management and semantic search capabilities for software development, enabling natural language queries to find relevant code snippets, functions, and classes across Python, JavaScript, TypeScript, and SQL codebases.

Enables AI agents to generate ticket IDs with QR codes, send tickets via email/SMS/WhatsApp, and retrieve event details through the Ticket Generator API.

Semantic navigator for MCP tools that finds relevant tools by natural language intent, reducing token usage by 95%.

Transforms free-text feature requests into well-formed tickets for Jira, GitHub, GitLab, and Linear, handling clarification, formatting, and duplicate detection while leaving the actual submission to the caller.

Enables running and learning from a production-shaped MCP server that exposes ticket-desk operations, server administration, timers, events, and schema-driven queries across stdio, SSE, and Streamable HTTP with configurable auth, rate limiting, and observability.

Enables playing a Workato-themed text-based RPG through MCP tools, allowing users to explore dungeons, battle enemies, and manage character progression in an integration-themed adventure.

A server that enables AI assistants to interact with Linear's project management tools through the Model Context Protocol, supporting features like searching, creating, and updating issues, adding comments, and retrieving user profiles and team information.

Enables support agents to look up customer account details and billing or incident information through two read-only MCP tools for triage workflows.

Enables interaction with Linear resources through an MCP interface, offering functionality for issue management and resource retrieval with rate limiting and error handling support.

Enables AI agents to manage Linear workspace resources such as issues, projects, teams, cycles, and comments via Streamable HTTP MCP, with LLM-optimized tools and batch operations.

Enables Boneyard to call authenticated, stateless Streamable HTTP tools for searching tickets and adding ticket comments, with caller context, attribution, and bounded results.

Enables agentic co-authoring of feature specs, decomposition into architecture-validated tickets, and idempotent syncing to Linear as projects with epics, sub-issues, and dependencies.

Cache-first MCP server for Linear that provides tools to search, read, create, update, and comment on issues and projects, caching data locally to reduce API calls and respect rate limits.

A Model Context Protocol server implementation that enables AI assistants to interact with Linear project management systems, allowing them to create, retrieve, and modify data related to issues, projects, teams, and users.

An MCP server that connects any two systems so an agent can explore both, map between them, and run real data transfers — then shows its working in a run folder you can audit afterwards.