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

Enables AI coding assistants to automatically scan, store, and query API endpoints from codebases, providing instant lookup and semantic search to reduce context switching and token consumption.

An MCP server that acts as a context-aware code retrieval broker for AI coding agents, enforcing backpressure and token budgeting to prevent context window overflow.

Enables LLMs to efficiently read, write, and refactor code using precise AST-based operations, reducing token usage and context window waste.

Make any LLM a codebase expert instantly. Provides deep code intelligence through semantic search, architecture mapping, security analysis, and smart context that fits perfectly in token windows.

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

A pipeline that segments source code into chunks, generates embeddings, and stores them in ChromaDB. It provides semantic context to AI coding assistants via the Model Context Protocol.

Enables AI models to interact with Linear for issue tracking and project management through the Model Context Protocol, supporting capabilities like creating issues, searching, managing sprints, and bulk updating statuses.

Enables teams to measure their actual ticket-writing style from GitLab, Jira, or GitHub and check new or draft tickets against that learned house profile, with optional model-backed drafting.

Provides code context and analysis for AI assistants by extracting directory structures and code symbols using WebAssembly Tree-sitter parsers with zero native dependencies.

An open-source Model Context Protocol server that enables interaction with the Linear API to manage issues, teams, and comments. It allows users to create, update, and search for issues while accessing workspace users and workflow states.

Enables managing Linear issues, projects, and teams through Cline. Supports CRUD operations, bulk actions, and rich text descriptions.

Indexes your codebase so AI coding agents can search instead of re-reading files, saving up to 94% of tokens.

Enables AI agents to interact with Linear workspaces by listing, retrieving, creating, and searching issues, and listing teams via the Linear GraphQL API.

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.

MCP server for Linear that allows creating issues, adding comments, and listing issues and teams.

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.

Indexes codebases into a SQLite database to provide metadata, exports, dependency graphs, and change tracking for JS/TS projects. It enables users to search for symbols, map internal dependencies, and monitor file changes through MCP tools and a web dashboard.

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 LLMs to perform addition calculations through a stdio-based MCP server with an add_numbers tool and type-safe validation.

Enables AI agents to manage a file-backed ticketing system directly within a local repository using a structured state machine and directory hierarchy. It enforces strict markdown schemas and provides specialized tools for claiming tasks, appending work logs, and validating ticket metadata.