Figma MCP Server vs Todoist MCP Server
Side-by-side comparison to help you choose.
| Feature | Figma MCP Server | Todoist MCP Server |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 46/100 | 46/100 |
| Adoption | 1 | 1 |
| Quality | 0 | 0 |
| Ecosystem |
| 1 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Reads and traverses Figma file hierarchies via the Figma REST API, exposing nested page, frame, component, and layer structures as queryable objects. Implements recursive tree traversal to map design document organization, enabling programmatic access to the complete design system hierarchy without manual file parsing.
Unique: Exposes Figma's hierarchical file structure as an MCP tool, allowing LLM agents to reason about design organization without requiring developers to write custom Figma API clients; integrates directly with Claude and other MCP-compatible tools.
vs alternatives: Simpler than building custom Figma API wrappers because it abstracts authentication and pagination; more accessible than raw Figma REST API because it's designed for agent-based querying rather than direct HTTP calls.
Extracts component definitions, variants, and properties from Figma files by querying the Figma API's component endpoints. Returns structured metadata including component names, variant configurations, property definitions, and documentation links, enabling design system introspection without manual inspection.
Unique: Provides structured component metadata queries via MCP, allowing agents to reason about component variants and properties without parsing Figma's REST API responses directly; includes variant flattening to expose all variant combinations as queryable entities.
vs alternatives: More accessible than Figma's raw REST API for component queries because it abstracts pagination and variant expansion; enables LLM agents to understand component structure without requiring developers to write custom parsing logic.
Extracts design tokens (colors, typography, spacing, shadows) from Figma files by parsing color styles, text styles, and effect styles via the Figma API. Converts Figma's native style definitions into standardized token formats (JSON, CSS variables, or design token schema), enabling design-to-code token synchronization.
Unique: Extracts Figma styles as queryable design tokens via MCP, enabling agents to reason about design system consistency and generate token files without manual export; supports multiple output formats for compatibility with design token platforms.
vs alternatives: More flexible than Figma's native export because it supports multiple output formats and can be integrated into automated pipelines; more accessible than building custom Figma API clients because authentication and style parsing are abstracted.
Queries detailed properties of frames and layers in Figma files, including dimensions, positioning, constraints, fill colors, strokes, effects, and text content. Implements property flattening to expose nested layer properties as queryable attributes, enabling design inspection and measurement extraction without manual Figma inspection.
Unique: Provides queryable layer and frame properties via MCP, allowing agents to extract design measurements and styling without parsing Figma's REST API responses; includes property flattening to expose nested attributes as top-level queryable fields.
vs alternatives: More accessible than Figma's REST API for property queries because it abstracts response parsing and property flattening; enables agents to reason about design measurements without requiring developers to write custom property extraction logic.
Registers Figma query capabilities as MCP tools with standardized JSON schemas, enabling Claude and other MCP-compatible clients to discover and invoke Figma operations through a unified tool interface. Implements schema validation to ensure tool inputs conform to expected types and constraints before API calls.
Unique: Implements MCP tool registration for Figma operations, allowing Claude and other MCP clients to invoke Figma queries as first-class tools without custom integration code; includes schema validation to ensure type safety and prevent malformed API calls.
vs alternatives: Simpler than building custom Claude plugins because it uses the standardized MCP protocol; more flexible than Figma's native integrations because it enables arbitrary agent-driven queries rather than pre-defined workflows.
Manages Figma API authentication by accepting and validating API tokens, implementing token refresh logic if needed, and handling authentication errors gracefully. Stores tokens securely in the MCP server environment and injects them into all Figma API requests, abstracting authentication complexity from tool consumers.
Unique: Abstracts Figma API authentication at the MCP server level, allowing tool consumers to invoke Figma operations without managing tokens directly; implements centralized token injection into all API requests.
vs alternatives: Simpler than managing Figma authentication in client code because tokens are configured once at the server level; more secure than embedding tokens in client applications because tokens are stored server-side only.
Implements error handling for Figma API failures, including rate limiting, authentication errors, and network timeouts. Returns structured error responses with diagnostic information, enabling tool consumers to understand failure reasons and implement retry logic. Includes timeout configuration to prevent hanging requests.
Unique: Provides structured error responses for Figma API failures, enabling tool consumers to implement intelligent retry logic and understand failure reasons; includes timeout configuration to prevent hanging requests.
vs alternatives: More informative than raw Figma API errors because it includes diagnostic context and retry guidance; more resilient than direct API calls because it abstracts error handling at the server level.
Enables querying across multiple Figma files within a team or project by accepting file IDs and aggregating results from multiple API calls. Implements batching to reduce API overhead and supports filtering to limit results to specific files or projects, enabling design system-wide queries without manual file enumeration.
Unique: Supports querying across multiple Figma files via a single MCP tool call, enabling agents to reason about design systems without manual file enumeration; implements batching to reduce API overhead.
vs alternatives: More efficient than making separate API calls per file because it batches requests and aggregates results; more accessible than building custom multi-file query logic because it abstracts file enumeration and result merging.
+2 more capabilities
Translates conversational task descriptions into structured Todoist API calls by parsing natural language for task content, due dates (e.g., 'tomorrow', 'next Monday'), priority levels (1-4 semantic mapping), and optional descriptions. Uses date recognition to convert human-readable temporal references into ISO format and priority mapping to interpret semantic priority language, then submits via Todoist REST API with full parameter validation.
Unique: Implements semantic date and priority parsing within the MCP tool handler itself, converting natural language directly to Todoist API parameters without requiring a separate NLP service or external date parsing library, reducing latency and external dependencies
vs alternatives: Faster than generic task creation APIs because date/priority parsing is embedded in the MCP handler rather than requiring round-trip calls to external NLP services or Claude for parameter extraction
Queries Todoist tasks using natural language filters (e.g., 'overdue tasks', 'tasks due this week', 'high priority tasks') by translating conversational filter expressions into Todoist API filter syntax. Supports partial name matching for task identification, date range filtering, priority filtering, and result limiting. Implements filter translation logic that converts semantic language into Todoist's native query parameter format before executing REST API calls.
Unique: Translates natural language filter expressions (e.g., 'overdue', 'this week') directly into Todoist API filter parameters within the MCP handler, avoiding the need for Claude to construct API syntax or make multiple round-trip calls to clarify filter intent
vs alternatives: More efficient than generic task APIs because filter translation is built into the MCP tool, reducing latency compared to systems that require Claude to generate filter syntax or make separate API calls to validate filter parameters
Figma MCP Server scores higher at 46/100 vs Todoist MCP Server at 46/100.
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Manages task organization by supporting project assignment and label association through Todoist API integration. Enables users to specify project_id when creating or updating tasks, and supports label assignment through task parameters. Implements project and label lookups to translate project/label names into IDs required by Todoist API, supporting task organization without requiring users to know numeric project IDs.
Unique: Integrates project and label management into task creation/update tools, allowing users to organize tasks by project and label without separate API calls, reducing friction in conversational task management
vs alternatives: More convenient than direct API project assignment because it supports project name lookup in addition to IDs, making it suitable for conversational interfaces where users reference projects by name
Packages the Todoist MCP server as an executable CLI binary (todoist-mcp-server) distributed via npm, enabling one-command installation and execution. Implements build process using TypeScript compilation (tsc) with executable permissions set via shx chmod +x, generating dist/index.js as the main entry point. Supports installation via npm install or Smithery package manager, with automatic binary availability in PATH after installation.
Unique: Distributes MCP server as an npm package with executable binary, enabling one-command installation and integration with Claude Desktop without manual configuration or build steps
vs alternatives: More accessible than manual installation because users can install with npm install @smithery/todoist-mcp-server, reducing setup friction compared to cloning repositories and building from source
Updates task attributes (name, description, due date, priority, project) by first identifying the target task using partial name matching against the task list, then applying the requested modifications via Todoist REST API. Implements a two-step process: (1) search for task by name fragment, (2) update matched task with new attribute values. Supports atomic updates of individual attributes without requiring full task replacement.
Unique: Implements client-side task identification via partial name matching before API update, allowing users to reference tasks by incomplete descriptions without requiring exact task IDs, reducing friction in conversational workflows
vs alternatives: More user-friendly than direct API updates because it accepts partial task names instead of requiring task IDs, making it suitable for conversational interfaces where users describe tasks naturally rather than providing identifiers
Marks tasks as complete by identifying the target task using partial name matching, then submitting a completion request to the Todoist API. Implements name-based task lookup followed by a completion API call, with optional status confirmation returned to the user. Supports completing tasks without requiring exact task IDs or manual task selection.
Unique: Combines task identification (partial name matching) with completion in a single MCP tool call, eliminating the need for separate lookup and completion steps, reducing round-trips in conversational task management workflows
vs alternatives: More efficient than generic task completion APIs because it integrates name-based task lookup, reducing the number of API calls and user interactions required to complete a task from a conversational description
Removes tasks from Todoist by identifying the target task using partial name matching, then submitting a deletion request to the Todoist API. Implements name-based task lookup followed by a delete API call, with confirmation returned to the user. Supports task removal without requiring exact task IDs, making deletion accessible through conversational interfaces.
Unique: Integrates name-based task identification with deletion in a single MCP tool call, allowing users to delete tasks by conversational description rather than task ID, reducing friction in task cleanup workflows
vs alternatives: More accessible than direct API deletion because it accepts partial task names instead of requiring task IDs, making it suitable for conversational interfaces where users describe tasks naturally
Implements the Model Context Protocol (MCP) server using stdio transport to enable bidirectional communication between Claude Desktop and the Todoist MCP server. Uses schema-based tool registration (CallToolRequestSchema) to define and validate tool parameters, with StdioServerTransport handling message serialization and deserialization. Implements the MCP server lifecycle (initialization, tool discovery, request handling) with proper error handling and type safety through TypeScript.
Unique: Implements MCP server with stdio transport and schema-based tool registration, providing a lightweight protocol bridge that requires no external dependencies beyond Node.js and the Todoist API, enabling direct Claude-to-Todoist integration without cloud intermediaries
vs alternatives: More lightweight than REST API wrappers because it uses stdio transport (no HTTP overhead) and integrates directly with Claude's MCP protocol, reducing latency and eliminating the need for separate API gateway infrastructure
+4 more capabilities