| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 5 decomposed |
| Times Matched | 0 | 0 |
Translates Model Context Protocol (MCP) tool calls received over stdio into authenticated GraphQL requests against the Linear API. The MCP Server Layer (src/mcp-server.ts) receives CallTool requests, routes them through a type-guarded tool dispatch system (src/tools/type-guards.ts validates arguments against Zod schemas), and delegates to LinearService which wraps @linear/sdk's LinearClient. This three-layer architecture decouples protocol handling from Linear SDK details, enabling AI clients (Cursor, Claude Desktop, Claude VSCode, GoMCP) to interact with Linear without custom integration code.
Unique: Three-layer architecture (MCP → Tool Dispatch → Linear Service) with strict separation of concerns: MCP protocol handling is isolated in src/mcp-server.ts, tool schemas and handlers are separated in src/tools/, and all Linear SDK access is funneled through a single LinearService class. This design enables adding new tools without touching protocol code and makes the codebase testable at each layer.
vs alternatives: Cleaner than custom REST API wrappers because it uses standard MCP protocol, reducing client-side integration work; more maintainable than monolithic Linear API clients because tool dispatch is schema-driven and handlers are decoupled from protocol details.
Registers 42 Linear tools (organized by domain: Issues, Projects, Initiatives, Cycles, Teams, Users/Org) via Zod schemas defined in src/tools/definitions/. Each tool has a corresponding handler in src/tools/handlers/ that receives validated arguments. The dispatch system uses type guards (src/tools/type-guards.ts) to validate incoming arguments against schemas before routing to handlers. Tool names follow a consistent linear_* prefix convention. This schema-first approach enables runtime validation, auto-documentation, and safe argument passing without manual type coercion.
Unique: Uses Zod schemas as the single source of truth for tool contracts — schemas define both validation rules and MCP tool descriptions, eliminating duplication. Type guards in src/tools/type-guards.ts enforce schema compliance before handler execution, preventing invalid data from reaching the Linear SDK.
vs alternatives: More robust than string-based argument parsing because Zod provides compile-time type safety and runtime validation; more maintainable than hand-written validators because schema changes automatically update both validation and documentation.
Exposes 18 issue-related tools covering the full lifecycle: create_issue, update_issue, delete_issue, get_issue, list_issues, search_issues, add_issue_comment, update_issue_comment, delete_issue_comment, and others. Each tool translates to a GraphQL operation via LinearService, which calls @linear/sdk's LinearClient. Handlers support filtering by status, assignee, project, cycle, and custom fields. The search_issues tool enables semantic and keyword-based queries. Comment operations support markdown formatting and attachment handling.
Unique: Issue tools are organized by operation type (create, update, delete, search, comment) with separate handlers for each, enabling fine-grained permission control and error handling. The search_issues tool integrates Linear's native search API rather than implementing custom indexing, reducing maintenance burden.
vs alternatives: More comprehensive than basic REST API wrappers because it exposes comment management and search alongside CRUD operations; more flexible than Linear's UI because it allows programmatic bulk operations and custom filtering.
Provides 5 project tools (get_project, list_projects, create_project, update_project, delete_project) and 10 initiative tools (get_initiative, list_initiatives, create_initiative, update_initiative, delete_initiative, link_initiative_to_project, etc.) that enable navigation and management of Linear's organizational hierarchy. Projects group issues; initiatives group projects. Handlers support filtering by team, status, and custom fields. The link_initiative_to_project tool enables cross-linking between organizational levels.
Unique: Separates project and initiative tools into distinct handlers, allowing AI agents to reason about organizational structure at different levels. The link_initiative_to_project tool enables cross-level operations without requiring the agent to manage IDs manually.
vs alternatives: More useful than flat issue lists because it exposes hierarchical structure, enabling agents to understand context before creating issues; more flexible than Linear's UI because it allows programmatic navigation and linking.
Exposes 3 cycle tools (get_cycle, list_cycles, create_cycle) that manage Linear's sprint/cycle abstraction. Cycles group issues into time-boxed iterations with start/end dates and status (planned, active, completed). Handlers support filtering by team and status. The create_cycle tool enables programmatic sprint planning. Cycles are the primary unit for iteration planning in Linear.
Unique: Exposes cycles as a first-class entity separate from issues, enabling agents to reason about sprint structure independently. The create_cycle tool uses ISO date strings for start/end times, enabling precise sprint planning without timezone confusion.
vs alternatives: More useful than issue-level cycle assignment because it enables agents to understand sprint structure upfront; simpler than custom sprint management tools because it leverages Linear's native cycle abstraction.
Provides 2 team/workflow tools (get_team, list_teams) that expose team metadata, workflow states, and status definitions. Handlers return team names, descriptions, members, and available workflow states (e.g., Backlog, Todo, In Progress, Done). This enables AI agents to understand team structure and valid issue states before creating or updating issues.
Unique: Exposes workflow states as part of team metadata, enabling agents to discover valid status values dynamically rather than hard-coding them. This makes the agent adaptable to custom Linear workflows.
vs alternatives: More flexible than hard-coded status lists because it discovers workflow states from the Linear API; more useful than team-only tools because it includes workflow configuration.
Provides 4 user/organization tools (get_user, list_users, get_organization, get_viewer) that resolve user identities and organization metadata. The get_viewer tool returns the authenticated user's identity (useful for determining who is running the agent). Handlers support filtering users by team or status. This enables agents to map user names to IDs and understand organization context.
Unique: The get_viewer tool provides a way for agents to determine their own identity without requiring the user to pass it explicitly. This is useful for audit trails and permission checks.
vs alternatives: More useful than static user lists because it discovers users dynamically from the Linear API; more secure than embedding user IDs because it resolves names at runtime.
Authenticates to Linear using Personal API Tokens supplied via environment variable (LINEAR_API_TOKEN) or CLI flag (--token), parsed by yargs in src/index.ts. The token is passed to @linear/sdk's LinearClient constructor and never stored by the server. Supports three deployment modes: npx (recommended), Docker, and Smithery. The runServer() function in src/index.ts bootstraps authentication before initializing the MCP server. No session management or token refresh logic is required because Linear tokens do not expire.
Unique: Supports three deployment modes (npx, Docker, Smithery) with consistent authentication logic, eliminating the need for environment-specific configuration. The token is resolved early in runServer() and never stored, reducing the attack surface.
vs alternatives: More flexible than hardcoded tokens because it supports environment variables and CLI flags; more secure than session-based auth because tokens are stateless and never persisted; more portable than custom deployment scripts because it supports standard deployment platforms.
+1 more capabilities
GitHub Copilot leverages the OpenAI Codex to provide real-time code suggestions based on the context of the current file and surrounding code. It analyzes the syntax and semantics of the code being written, utilizing a transformer-based architecture that allows it to understand and predict the next lines of code effectively. This context-awareness is enhanced by its ability to learn from the user's coding style over time, making suggestions more relevant and personalized.
Unique: Utilizes a transformer model trained on a diverse dataset of public code repositories, allowing for nuanced understanding of coding patterns.
vs alternatives: More contextually aware than traditional autocomplete tools due to its deep learning foundation and extensive training data.
Copilot supports multiple programming languages by employing a language-agnostic model that can generate code snippets across various languages. It identifies the programming language in use through file extensions and syntax cues, allowing it to adapt its suggestions accordingly. This capability is powered by a unified model that has been trained on code from numerous languages, enabling seamless transitions between different coding environments.
Unique: Employs a single model architecture that can generate code across various languages without needing separate models for each language.
vs alternatives: More versatile than many IDE-specific tools that only support a limited set of languages.
GitHub Copilot can generate entire functions or methods based on comments or partial code snippets provided by the user. It interprets the intent behind the comments, using natural language processing to translate user descriptions into functional code. This capability is particularly useful for boilerplate code generation, allowing developers to focus on more complex logic while Copilot handles repetitive tasks.
GitHub Copilot scores higher at 47/100 vs Linear at 40/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Unique: Integrates natural language understanding to convert user comments into structured code, enhancing productivity in function creation.
vs alternatives: More intuitive than traditional code generators that require explicit parameters and structures.
Copilot enables real-time collaboration by providing suggestions that adapt to the contributions of multiple developers in a shared coding environment. It processes input from all collaborators and generates contextually relevant suggestions that consider the collective coding style and ongoing changes. This feature is particularly beneficial in pair programming or team coding sessions, where maintaining coherence in code style is crucial.
Unique: Utilizes a shared context mechanism to provide collaborative suggestions, enhancing team productivity and code coherence.
vs alternatives: More effective in collaborative settings than static code completion tools that do not account for multiple contributors.
GitHub Copilot can generate documentation comments for functions and classes based on their implementation and purpose inferred from the code. It analyzes the code structure and uses natural language generation to create clear, concise documentation that explains the functionality. This capability helps developers maintain better documentation practices without requiring additional effort.
Unique: Combines code analysis with natural language generation to produce documentation that is directly relevant to the code's context.
vs alternatives: More integrated than standalone documentation tools that require separate input and context.