Asana MCP Server vs Todoist MCP Server
Side-by-side comparison to help you choose.
| Feature | Asana MCP Server | Todoist MCP Server |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 43/100 | 44/100 |
| Adoption | 1 | 1 |
| Quality | 0 | 0 |
| Ecosystem |
| 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes asana_create_task tool through MCP protocol that accepts JSON schema-validated parameters (name, description, assignee, due_date, projects, tags) and translates them into Asana REST API POST requests. Uses TypeScript type definitions and runtime validation to ensure only valid Asana field types are submitted, preventing malformed API calls before they reach Asana's servers.
Unique: Implements MCP tool registration with Asana-specific schema constraints (e.g., due_on as ISO8601 string, projects as array of GIDs) rather than generic REST wrapper, enabling AI clients to understand valid parameter combinations without trial-and-error API calls
vs alternatives: Tighter validation than raw Asana API SDKs because schema is enforced at MCP protocol layer before reaching Asana, reducing failed requests and API quota waste
Implements asana_search_tasks tool that queries Asana's task search endpoint with filters across workspace, project, assignee, tag, and status fields. Translates MCP parameters into Asana's query syntax (e.g., 'assignee:gid' format) and returns paginated results with task metadata (GID, name, assignee, due date, completion status). Supports both simple text search and structured field-based filtering.
Unique: Translates natural MCP filter parameters into Asana's proprietary query syntax (e.g., 'assignee:gid' format) transparently, allowing AI clients to use simple field names without learning Asana's search grammar
vs alternatives: More discoverable than raw Asana API because MCP schema explicitly lists available filter fields, whereas Asana's REST API requires reading documentation to know which query operators are valid
Exposes asana_get_project tool that retrieves full project metadata including sections array with section GIDs and names. Sections are organizational containers within projects that group related tasks. Tool returns section structure enabling AI clients to understand project layout and determine correct section for task placement. Sections are read-only through MCP — creation/deletion not supported.
Unique: Exposes sections as part of project metadata rather than separate tool, allowing AI clients to discover section structure in single call and understand project workflow layout
vs alternatives: More efficient than separate section-listing tool because sections are included in project metadata, reducing API calls needed to understand project organization
Implements asana_update_task tool that modifies specific task fields (name, description, assignee, due_date, completed status, custom fields) through Asana's PATCH endpoint. Uses selective field update pattern — only provided fields are modified, leaving others unchanged. Validates field types before submission and returns updated task metadata. Supports both simple fields (name, description) and complex fields (custom fields, assignee).
Unique: Implements selective field updates using PATCH semantics rather than full task replacement, allowing AI agents to modify single fields without risk of overwriting other task data
vs alternatives: Safer than full task replacement because only specified fields are modified, reducing risk of accidental data loss if AI agent doesn't include all fields in update request
Exposes asana_get_workspaces tool that lists all workspaces accessible to the authenticated user. Returns workspace metadata (GID, name, is_organization) enabling AI clients to discover available workspaces and select correct workspace for subsequent operations. Workspace enumeration is required step before accessing projects or tasks since all Asana entities are scoped to workspaces.
Unique: Provides workspace enumeration as dedicated tool rather than requiring users to hardcode workspace GIDs, enabling dynamic workspace discovery for multi-workspace organizations
vs alternatives: More flexible than hardcoded workspace GIDs because AI agents can discover available workspaces at runtime and select appropriate workspace for operations
Implements asana_get_task tool that retrieves complete task metadata including standard fields (name, description, assignee, due_date, completed) and custom fields with their values. Uses Asana's task detail endpoint with field expansion to include related data (assignee details, project info, custom field definitions and values). Returns comprehensive task context enabling AI clients to understand full task state.
Unique: Includes custom field expansion in task retrieval, returning both field definitions and values in single call, rather than requiring separate custom field metadata lookups
vs alternatives: More complete than basic task retrieval because custom fields are included with values, enabling AI agents to make decisions based on custom metadata without additional API calls
Implements error handling layer that catches Asana API errors (4xx, 5xx responses) and validation errors (invalid parameters, missing required fields) and returns structured error responses through MCP protocol. Maps Asana API error codes to human-readable messages and includes error context (which field failed, why) enabling AI clients to understand failure reasons and retry appropriately. Validation happens before API calls to prevent wasted requests.
Unique: Validates parameters at MCP schema layer before submitting to Asana API, catching invalid inputs early and reducing failed API calls and quota waste
vs alternatives: More efficient than API-first validation because schema validation prevents invalid requests from reaching Asana, reducing API quota consumption and latency
Exposes asana_add_task_dependency and asana_remove_task_dependency tools that manage task blocking relationships through Asana's dependency API. Translates MCP requests into Asana's dependency endpoint calls, supporting 'blocks' and 'is_blocked_by' relationship types. Validates that both task GIDs exist before attempting relationship creation, preventing orphaned dependencies.
Unique: Wraps Asana's dependency API with explicit relationship type parameters ('blocks' vs 'is_blocked_by') in MCP schema, making directionality unambiguous for AI agents that might otherwise confuse blocking semantics
vs alternatives: Clearer than Asana's native UI for programmatic dependency creation because MCP schema forces explicit relationship direction, whereas UI can be ambiguous about which task blocks which
+7 more capabilities
Translates conversational task descriptions into structured Todoist API calls by parsing natural language for task content, due dates, priority levels, project assignments, and labels. Uses date recognition to convert phrases like 'tomorrow' or 'next Monday' into ISO format, and maps semantic priority descriptions (e.g., 'high', 'urgent') to Todoist's 1-4 priority scale. Implements MCP tool schema validation to ensure all parameters conform to Todoist API requirements before transmission.
Unique: Implements MCP tool schema binding that allows Claude to directly invoke todoist_create_task with natural language understanding of date parsing and priority mapping, rather than requiring users to manually specify ISO dates or numeric priority codes. Uses Todoist REST API v2 with full parameter validation before submission.
vs alternatives: More conversational than raw Todoist API calls because Claude's language understanding handles date/priority translation automatically, whereas direct API integration requires users to format parameters explicitly.
Executes structured queries against Todoist's task database by translating natural language filters (e.g., 'tasks due today', 'overdue items in project X', 'high priority tasks') into Todoist API filter syntax. Supports filtering by due date ranges, project, label, priority, and completion status. Implements result limiting and pagination to prevent overwhelming response sizes. The server parses natural language date expressions and converts them to Todoist's filter query language before API submission.
Unique: Implements MCP tool binding for todoist_get_tasks that translates Claude's natural language filter requests into Todoist's native filter query syntax, enabling semantic task retrieval without requiring users to learn Todoist's filter language. Includes date parsing for relative expressions like 'this week' or 'next 3 days'.
vs alternatives: More user-friendly than raw Todoist API filtering because Claude handles natural language interpretation of date ranges and filter logic, whereas direct API calls require users to construct filter strings manually.
Todoist MCP Server scores higher at 44/100 vs Asana MCP Server at 43/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Catches HTTP errors from Todoist API calls and translates them into user-friendly error messages that Claude can understand and communicate to users. Handles common error scenarios (invalid token, rate limiting, malformed requests, server errors) with appropriate error codes and descriptions. Implements retry logic for transient errors (5xx responses) and provides clear feedback for permanent errors (4xx responses).
Unique: Implements HTTP error handling that translates Todoist API error responses into user-friendly messages that Claude can understand and communicate. Includes basic retry logic for transient errors (5xx responses) and clear feedback for permanent errors (4xx responses).
vs alternatives: More user-friendly than raw HTTP error codes because error messages are translated to natural language, though less robust than production error handling with exponential backoff and circuit breakers.
Implements substring and fuzzy matching logic to identify tasks by partial or approximate names, reducing the need for exact task IDs. Uses case-insensitive matching and handles common variations (e.g., extra spaces, punctuation differences). Returns the best matching task when multiple candidates exist, with confidence scoring to help Claude disambiguate if needed.
Unique: Implements fuzzy matching logic that identifies tasks by partial or approximate names without requiring exact IDs, enabling conversational task references. Uses case-insensitive matching and confidence scoring to handle ambiguous cases.
vs alternatives: More user-friendly than ID-based task identification because users can reference tasks by name, though less reliable than exact ID matching because fuzzy matching may identify wrong task if names are similar.
Implements MCP server using stdio transport to communicate with Claude Desktop via standard input/output streams. Handles MCP protocol serialization/deserialization of JSON-RPC messages, tool invocation routing, and response formatting. Manages the lifecycle of the stdio connection and handles graceful shutdown on client disconnect.
Unique: Implements MCP server using stdio transport with JSON-RPC message handling, enabling Claude Desktop to invoke Todoist operations through standardized MCP protocol. Uses StdioServerTransport from MCP SDK for protocol handling.
vs alternatives: Simpler than HTTP-based MCP servers because stdio transport doesn't require network configuration, though less flexible because it's limited to local Claude Desktop integration.
Updates task properties (name, description, due date, priority, project, labels) by first performing partial name matching to locate the target task, then submitting attribute changes to the Todoist API. Uses fuzzy matching or substring search to identify tasks from incomplete descriptions, reducing the need for exact task IDs. Validates all updated attributes against Todoist API schema before submission and returns confirmation of changes applied.
Unique: Implements MCP tool binding for todoist_update_task that uses name-based task identification rather than requiring task IDs, enabling Claude to modify tasks through conversational references. Includes fuzzy matching logic to handle partial or approximate task names.
vs alternatives: More conversational than Todoist API's ID-based updates because users can reference tasks by name rather than looking up numeric IDs, though this adds latency for the name-matching lookup step.
Marks tasks as complete by first identifying them through partial name matching, then submitting completion status to the Todoist API. Implements fuzzy matching to locate tasks from incomplete or approximate descriptions, reducing friction in conversational workflows. Returns confirmation of completion status and task metadata to confirm the action succeeded.
Unique: Implements MCP tool binding for todoist_complete_task that uses partial name matching to identify tasks, allowing Claude to complete tasks through conversational references without requiring task IDs. Includes confirmation feedback to prevent accidental completions.
vs alternatives: More user-friendly than Todoist API's ID-based completion because users can reference tasks by name, though the name-matching step adds latency compared to direct ID-based completion.
Removes tasks from Todoist by first identifying them through partial name matching, then submitting deletion requests to the Todoist API. Implements fuzzy matching to locate tasks from incomplete descriptions. Provides confirmation feedback to acknowledge successful deletion and prevent accidental removals.
Unique: Implements MCP tool binding for todoist_delete_task that uses partial name matching to identify tasks, allowing Claude to delete tasks through conversational references. Includes confirmation feedback to acknowledge deletion.
vs alternatives: More conversational than Todoist API's ID-based deletion because users can reference tasks by name, though the name-matching step adds latency and deletion risk if names are ambiguous.
+5 more capabilities