{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-themotionmachine-omnifocus-mcp","slug":"mcp-themotionmachine-omnifocus-mcp","name":"OmniFocus-MCP","type":"mcp","url":"https://github.com/themotionmachine/OmniFocus-MCP","page_url":"https://unfragile.ai/mcp-themotionmachine-omnifocus-mcp","categories":["mcp-servers"],"tags":["mcp","mcp-server","modelcontextprotocol","omnifocus","task-management"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-themotionmachine-omnifocus-mcp__cap_0","uri":"capability://data.processing.analysis.mcp.based.task.database.query.with.filtering.and.sorting","name":"mcp-based task database query with filtering and sorting","description":"Executes targeted queries against the OmniFocus database through AppleScript/JXA automation, supporting field selection, filtering predicates, and sorting parameters to retrieve specific task subsets without full database export. The query_omnifocus tool implements a schema-based filtering interface that translates LLM parameters into AppleScript queries, enabling efficient retrieval of tasks by status, project, due date, and custom metadata without loading the entire database into context.","intents":["I want to find all overdue tasks without exporting my entire OmniFocus database","I need to retrieve tasks from a specific project with particular tags or flags","I want to query tasks by due date range or completion status to prioritize work","I need to fetch task metadata (name, notes, due date, project) for a filtered subset"],"best_for":["AI assistants (Claude, etc.) integrated with OmniFocus seeking efficient task queries","LLM-powered task analysis agents that need to avoid context bloat from full database dumps","Developers building task-aware AI workflows on macOS"],"limitations":["AppleScript execution adds 200-500ms latency per query due to macOS process overhead","Query complexity limited by AppleScript predicate syntax; complex boolean logic may require multiple queries","macOS-only; no Windows or Linux support due to OmniFocus platform constraints","Real-time filtering happens on the OmniFocus process, not in the MCP server, so large result sets still consume context"],"requires":["macOS with OmniFocus 3.x or later installed","Node.js 18+ for MCP server runtime","Claude Desktop or compatible MCP client configured with stdio transport"],"input_types":["JSON schema with filter parameters (status, project, tags, due date range, custom fields)","Sort field and direction (ascending/descending)"],"output_types":["JSON array of task objects with selected fields (id, name, notes, dueDate, project, tags, flagged, completed)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-themotionmachine-omnifocus-mcp__cap_1","uri":"capability://data.processing.analysis.complete.omnifocus.database.export.and.state.snapshot","name":"complete omnifocus database export and state snapshot","description":"Exports the entire OmniFocus database as a structured JSON snapshot through AppleScript automation, providing comprehensive access to all tasks, projects, folders, and metadata for holistic analysis or context-aware reasoning. The dump_database tool serializes the complete OmniFocus object hierarchy (projects, tasks, contexts, perspectives) into a single JSON payload, enabling LLMs to perform cross-cutting analysis, dependency detection, or full-system optimization in a single context window.","intents":["I want to analyze my entire task system for bottlenecks, dependencies, or organizational patterns","I need to provide my AI assistant with complete context about my task structure for intelligent prioritization","I want to export my OmniFocus state for backup, migration, or external processing","I need to detect task conflicts, circular dependencies, or resource allocation issues across all projects"],"best_for":["LLM agents performing system-wide task analysis or optimization","Users seeking comprehensive context for AI-assisted task restructuring","Developers building OmniFocus data export pipelines or integrations"],"limitations":["Full database export can exceed context window limits for large OmniFocus instances (1000+ tasks); requires external pagination or chunking","AppleScript serialization adds 1-3 second latency for large databases due to object traversal overhead","No incremental/delta export; always returns complete state, wasting context on unchanged data","macOS-only; OmniFocus database format is proprietary and not directly accessible on other platforms"],"requires":["macOS with OmniFocus 3.x or later installed","Node.js 18+ for MCP server runtime","Claude Desktop or compatible MCP client with sufficient context window (8k+ tokens recommended for large databases)"],"input_types":["No parameters; exports entire database state"],"output_types":["JSON object containing nested structure: { projects: [...], tasks: [...], folders: [...], contexts: [...], perspectives: [...] } with full metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-themotionmachine-omnifocus-mcp__cap_10","uri":"capability://safety.moderation.schema.based.tool.parameter.validation.and.mcp.protocol.compliance","name":"schema-based tool parameter validation and mcp protocol compliance","description":"Validates tool parameters against JSON schemas before execution, ensuring MCP protocol compliance and preventing invalid requests from reaching AppleScript execution. Each tool defines a schema that specifies required/optional parameters, types, and constraints (e.g., ISO 8601 date format, valid project names), and the MCP server validates incoming requests against these schemas before invoking handlers. This approach provides early error detection, clear error messages to LLM clients, and prevents malformed AppleScript commands from being generated.","intents":["I want to ensure that tool parameters are validated before AppleScript execution to prevent errors","I need clear error messages when LLMs provide invalid parameters (e.g., malformed dates, missing required fields)","I want to enforce parameter constraints (e.g., valid project names, ISO 8601 dates) at the MCP protocol level","I need to ensure MCP protocol compliance for interoperability with different LLM clients"],"best_for":["MCP server implementations requiring robust parameter validation","LLM integrations that need clear error feedback for invalid requests","Developers building MCP tools with strict parameter requirements"],"limitations":["Schema validation only checks format and type; cannot validate semantic constraints (e.g., whether a project name actually exists in OmniFocus)","Error messages from schema validation may be generic and not actionable for LLM clients","Schema definitions are static and must be manually updated if tool parameters change","No runtime type coercion; strict type matching may reject valid inputs that could be coerced (e.g., numeric strings as dates)"],"requires":["@modelcontextprotocol/sdk with schema validation support","JSON schema definitions for each tool (included in tool handler modules)"],"input_types":["JSON objects matching tool parameter schemas"],"output_types":["Validation success (tool execution proceeds) or validation error with detailed message"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-themotionmachine-omnifocus-mcp__cap_2","uri":"capability://tool.use.integration.natural.language.task.creation.with.project.and.metadata.assignment","name":"natural language task creation with project and metadata assignment","description":"Creates individual tasks in OmniFocus through AppleScript automation, accepting natural language task names and optional structured metadata (project assignment, due dates, tags, notes, priority flags) via MCP schema parameters. The add_omnifocus_task tool translates LLM-generated task specifications into AppleScript commands that instantiate new task objects with full metadata binding, enabling AI assistants to create tasks with context-aware properties without requiring users to manually enter details.","intents":["I want my AI assistant to create a task with a specific due date, project, and tags based on conversation context","I need to add a task with detailed notes or description from an AI-generated summary","I want to create a task and immediately assign it to a project and set its priority","I need to batch-create related tasks with consistent metadata (same project, tags, or due date)"],"best_for":["LLM-powered task capture agents that infer metadata from conversation","AI assistants augmenting user workflows with automatic task creation","Developers building conversational task management interfaces"],"limitations":["AppleScript execution adds 200-400ms per task creation; batch operations require sequential execution unless using batch_add_items","Project and tag assignment requires exact name matching; fuzzy matching not supported, so typos cause failures","No validation of due date format before AppleScript execution; invalid dates fail silently or create tasks with default dates","Cannot create tasks with custom fields or advanced OmniFocus features (repeating patterns, sequential dependencies) through this tool"],"requires":["macOS with OmniFocus 3.x or later installed","Node.js 18+ for MCP server runtime","Valid project name if assigning to a project (must match existing OmniFocus project exactly)","Optional: valid tag names, ISO 8601 due date format"],"input_types":["JSON schema with: taskName (string, required), projectName (string, optional), dueDate (ISO 8601 string, optional), tags (array of strings, optional), notes (string, optional), flagged (boolean, optional)"],"output_types":["JSON object with created task metadata: { id, name, projectName, dueDate, tags, notes, flagged, createdDate }"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-themotionmachine-omnifocus-mcp__cap_3","uri":"capability://tool.use.integration.project.creation.and.hierarchical.organization","name":"project creation and hierarchical organization","description":"Creates new projects in OmniFocus with optional parent project assignment, enabling hierarchical task organization through AppleScript automation. The add_project tool accepts project names and parent project references, translating them into AppleScript commands that instantiate project objects with proper hierarchy binding, allowing AI assistants to organize tasks into logical groupings and sub-projects based on conversation context or task analysis.","intents":["I want my AI assistant to create a new project for a specific initiative and optionally nest it under a parent project","I need to organize tasks into a project hierarchy based on AI-inferred categories or goals","I want to create a project and immediately populate it with related tasks","I need to establish a project structure that mirrors a business workflow or organizational unit"],"best_for":["LLM agents performing task organization or project planning","AI assistants that infer project structures from conversation or task analysis","Developers building AI-powered project management workflows"],"limitations":["Parent project assignment requires exact name matching; no fuzzy matching or ID-based references","Cannot set project properties like status, review frequency, or custom fields through this tool","AppleScript execution adds 200-400ms per project creation; creating deep hierarchies requires sequential calls","No validation that parent project exists before creation; fails silently if parent name is incorrect"],"requires":["macOS with OmniFocus 3.x or later installed","Node.js 18+ for MCP server runtime","Valid parent project name if assigning hierarchy (must match existing OmniFocus project exactly)"],"input_types":["JSON schema with: projectName (string, required), parentProjectName (string, optional)"],"output_types":["JSON object with created project metadata: { id, name, parentProjectName, createdDate }"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-themotionmachine-omnifocus-mcp__cap_4","uri":"capability://tool.use.integration.task.and.project.item.modification.with.field.level.updates","name":"task and project item modification with field-level updates","description":"Updates existing OmniFocus tasks or projects by modifying specific fields (name, due date, project assignment, tags, notes, completion status, priority flags) through AppleScript automation. The editItem tool accepts item IDs and a map of field updates, translating them into AppleScript property assignments that modify task/project objects in-place, enabling AI assistants to refine task details, reschedule work, or update metadata based on conversation context without full item replacement.","intents":["I want to update a task's due date, project, or tags based on new information from conversation","I need to mark a task as complete or change its priority flag based on AI analysis","I want to update task notes or description with AI-generated summaries or context","I need to reassign a task to a different project or add tags based on AI categorization"],"best_for":["LLM agents refining task metadata based on conversation or analysis","AI assistants updating task state in response to user feedback","Developers building task modification workflows triggered by AI reasoning"],"limitations":["Requires exact item ID; no name-based lookup, so LLMs must query first to obtain IDs","Project reassignment requires exact project name matching; no fuzzy matching","Cannot update computed fields (createdDate, completedDate) or advanced properties (recurrence patterns, sequential dependencies)","AppleScript property assignment adds 200-400ms per update; bulk updates require sequential calls unless using batch operations"],"requires":["macOS with OmniFocus 3.x or later installed","Node.js 18+ for MCP server runtime","Valid item ID (obtained from query_omnifocus or dump_database)","Valid project name if reassigning (must match existing OmniFocus project exactly)"],"input_types":["JSON schema with: itemId (string, required), updates (object with optional fields: name, dueDate, projectName, tags, notes, completed, flagged)"],"output_types":["JSON object with updated item metadata: { id, name, projectName, dueDate, tags, notes, completed, flagged, modifiedDate }"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-themotionmachine-omnifocus-mcp__cap_5","uri":"capability://tool.use.integration.task.and.project.deletion.with.cascading.removal","name":"task and project deletion with cascading removal","description":"Removes tasks or projects from OmniFocus through AppleScript automation, supporting both individual item deletion and cascading removal of nested items (deleting a project removes all contained tasks). The removeItem tool accepts item IDs and executes AppleScript delete commands that remove objects from the OmniFocus database, enabling AI assistants to clean up completed work, archive obsolete projects, or reorganize task structures based on analysis or user intent.","intents":["I want to delete a completed task or obsolete project from OmniFocus","I need to remove a project and all its nested tasks based on AI analysis or user request","I want to clean up duplicate or conflicting tasks identified by an AI agent","I need to archive or remove tasks that no longer align with current goals"],"best_for":["LLM agents performing task cleanup or project archival","AI assistants removing obsolete items based on analysis or user feedback","Developers building task management workflows with deletion capabilities"],"limitations":["Deletion is permanent and non-recoverable through MCP; no soft-delete or trash functionality","Cascading deletion of nested items is automatic but not granular; cannot selectively preserve child tasks when deleting a project","Requires exact item ID; no name-based lookup or batch deletion by criteria","AppleScript execution adds 200-400ms per deletion; bulk deletions require sequential calls unless using batch_remove_items"],"requires":["macOS with OmniFocus 3.x or later installed","Node.js 18+ for MCP server runtime","Valid item ID (obtained from query_omnifocus or dump_database)"],"input_types":["JSON schema with: itemId (string, required)"],"output_types":["JSON object with deletion confirmation: { itemId, deleted: true, deletedDate }"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-themotionmachine-omnifocus-mcp__cap_6","uri":"capability://tool.use.integration.batch.task.creation.with.bulk.metadata.assignment","name":"batch task creation with bulk metadata assignment","description":"Creates multiple tasks in OmniFocus in a single MCP call through AppleScript automation, accepting an array of task specifications with metadata (names, due dates, projects, tags, notes) and executing sequential AppleScript commands to instantiate all tasks atomically. The batch_add_items tool optimizes bulk task creation by reducing MCP round-trips and AppleScript process overhead compared to individual add_omnifocus_task calls, enabling AI assistants to populate projects with related tasks or implement task breakdowns in a single operation.","intents":["I want to create multiple related tasks (e.g., subtasks for a project) in a single operation","I need to bulk-import tasks from an AI-generated task breakdown or project plan","I want to populate a new project with a set of tasks and consistent metadata in one call","I need to create a batch of tasks with the same project, due date, or tags efficiently"],"best_for":["LLM agents generating task breakdowns or project plans that require bulk creation","AI assistants populating projects with multiple related tasks","Developers building task import or migration workflows"],"limitations":["Sequential AppleScript execution means latency scales with batch size (~200-400ms per task); large batches (100+) may exceed timeout thresholds","No transactional semantics; partial failures leave some tasks created while others fail, requiring manual cleanup","Project and tag assignment requires exact name matching for all items; a single typo fails the entire batch","No validation of task specifications before execution; invalid data causes AppleScript errors mid-batch"],"requires":["macOS with OmniFocus 3.x or later installed","Node.js 18+ for MCP server runtime","Valid project names if assigning to projects (must match existing OmniFocus projects exactly)","Array of task specifications with required taskName field and optional metadata fields"],"input_types":["JSON schema with: items (array of objects, each with: taskName (string, required), projectName (string, optional), dueDate (ISO 8601 string, optional), tags (array of strings, optional), notes (string, optional), flagged (boolean, optional))"],"output_types":["JSON array of created task objects: [{ id, name, projectName, dueDate, tags, notes, flagged, createdDate }, ...]"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-themotionmachine-omnifocus-mcp__cap_7","uri":"capability://tool.use.integration.batch.task.and.project.deletion.with.bulk.removal","name":"batch task and project deletion with bulk removal","description":"Removes multiple tasks or projects from OmniFocus in a single MCP call through sequential AppleScript execution, accepting an array of item IDs and executing delete commands for all items atomically. The batch_remove_items tool optimizes bulk deletion by reducing MCP round-trips and AppleScript process overhead compared to individual removeItem calls, enabling AI assistants to clean up large sets of obsolete items or reorganize task structures efficiently.","intents":["I want to delete multiple completed tasks or obsolete projects in a single operation","I need to bulk-remove items identified by an AI agent as duplicates or conflicts","I want to clean up a large set of tasks based on AI analysis (e.g., all tasks older than a date)","I need to reorganize my task structure by removing multiple items efficiently"],"best_for":["LLM agents performing bulk task cleanup or project archival","AI assistants removing large sets of obsolete items based on analysis","Developers building task management workflows with bulk deletion capabilities"],"limitations":["Sequential AppleScript execution means latency scales with batch size (~200-400ms per item); large batches (100+) may exceed timeout thresholds","Deletion is permanent and non-recoverable; no transactional rollback if partial failures occur","No transactional semantics; partial failures leave some items deleted while others remain, requiring manual verification","Requires exact item IDs; no criteria-based deletion (e.g., delete all tasks with tag X), so LLMs must query first to obtain IDs"],"requires":["macOS with OmniFocus 3.x or later installed","Node.js 18+ for MCP server runtime","Valid item IDs (obtained from query_omnifocus or dump_database)"],"input_types":["JSON schema with: itemIds (array of strings, required)"],"output_types":["JSON object with deletion summary: { deletedCount: number, deletedIds: [string, ...], deletedDate }"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-themotionmachine-omnifocus-mcp__cap_8","uri":"capability://tool.use.integration.mcp.server.lifecycle.management.and.tool.registration","name":"mcp server lifecycle management and tool registration","description":"Manages the OmniFocus MCP server initialization, tool registration, and stdio transport setup, implementing the Model Context Protocol specification through the @modelcontextprotocol/sdk framework. The server instantiates an McpServer object, registers eight tools with their schemas and handlers, establishes StdioServerTransport for process-based communication with AI assistants (Claude, etc.), and handles connection lifecycle with error handling, enabling seamless integration between LLMs and OmniFocus through standardized MCP protocol.","intents":["I want to set up an MCP server that exposes OmniFocus capabilities to Claude or other LLM clients","I need to configure the OmniFocus MCP server to run as a background process and accept MCP protocol requests","I want to ensure the MCP server properly registers all tools and handles client connections reliably","I need to debug or monitor the MCP server's tool registration and connection lifecycle"],"best_for":["Developers integrating OmniFocus with Claude Desktop or other MCP-compatible LLM clients","Users setting up local MCP servers for task management automation","Teams building custom LLM applications that require OmniFocus integration"],"limitations":["Stdio transport is process-based and single-connection; does not support concurrent client connections","Tool registration is static at server startup; adding new tools requires server restart","Error handling in connection lifecycle may mask underlying AppleScript failures; debugging requires server logs","No built-in authentication or authorization; assumes trusted local execution environment"],"requires":["Node.js 18+ for MCP server runtime","@modelcontextprotocol/sdk npm package (included in package.json)","macOS with OmniFocus 3.x or later for AppleScript integration","Claude Desktop 0.1.0+ or compatible MCP client configured with stdio transport"],"input_types":["MCP protocol messages (tool calls with parameters)"],"output_types":["MCP protocol responses (tool results or errors)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-themotionmachine-omnifocus-mcp__cap_9","uri":"capability://tool.use.integration.applescript.based.omnifocus.process.automation.and.object.serialization","name":"applescript-based omnifocus process automation and object serialization","description":"Executes AppleScript/JXA commands to interact with the OmniFocus application process, enabling task/project CRUD operations and database serialization through direct object model access. The architecture uses AppleScript as the integration layer between the Node.js MCP server and the OmniFocus application, translating tool parameters into AppleScript commands that instantiate, modify, query, or delete OmniFocus objects, and serializing results back to JSON for LLM consumption. This approach leverages OmniFocus's scriptable object model (projects, tasks, contexts, perspectives) to provide programmatic access without requiring database file manipulation.","intents":["I want to interact with OmniFocus programmatically without direct database file access","I need to execute OmniFocus operations (create, read, update, delete) through a standardized interface","I want to serialize OmniFocus data structures to JSON for LLM processing","I need to ensure OmniFocus operations are atomic and properly sequenced through AppleScript"],"best_for":["macOS-based LLM integrations requiring OmniFocus automation","Developers building task management workflows that leverage OmniFocus's scriptable object model","Teams seeking programmatic OmniFocus access without database-level manipulation"],"limitations":["AppleScript execution adds 200-500ms latency per operation due to macOS process overhead and object serialization","AppleScript error handling is limited; failures may not provide detailed error messages, complicating debugging","OmniFocus object model is proprietary and version-dependent; API changes in OmniFocus updates may break scripts","No parallel execution; AppleScript commands are sequential, limiting throughput for bulk operations","macOS-only; no Windows or Linux support due to OmniFocus platform constraints and AppleScript availability"],"requires":["macOS with OmniFocus 3.x or later installed and running","Node.js 18+ with AppleScript execution capability (native on macOS)","Appropriate macOS permissions for AppleScript to access OmniFocus (may require accessibility permissions)"],"input_types":["Tool parameters (JSON schema) that are translated to AppleScript commands"],"output_types":["JSON serialization of OmniFocus objects (tasks, projects, metadata)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["macOS with OmniFocus 3.x or later installed","Node.js 18+ for MCP server runtime","Claude Desktop or compatible MCP client configured with stdio transport","Claude Desktop or compatible MCP client with sufficient context window (8k+ tokens recommended for large databases)","@modelcontextprotocol/sdk with schema validation support","JSON schema definitions for each tool (included in tool handler modules)","Valid project name if assigning to a project (must match existing OmniFocus project exactly)","Optional: valid tag names, ISO 8601 due date format","Valid parent project name if assigning hierarchy (must match existing OmniFocus project exactly)","Valid item ID (obtained from query_omnifocus or dump_database)"],"failure_modes":["AppleScript execution adds 200-500ms latency per query due to macOS process overhead","Query complexity limited by AppleScript predicate syntax; complex boolean logic may require multiple queries","macOS-only; no Windows or Linux support due to OmniFocus platform constraints","Real-time filtering happens on the OmniFocus process, not in the MCP server, so large result sets still consume context","Full database export can exceed context window limits for large OmniFocus instances (1000+ tasks); requires external pagination or chunking","AppleScript serialization adds 1-3 second latency for large databases due to object traversal overhead","No incremental/delta export; always returns complete state, wasting context on unchanged data","macOS-only; OmniFocus database format is proprietary and not directly accessible on other platforms","Schema validation only checks format and type; cannot validate semantic constraints (e.g., whether a project name actually exists in OmniFocus)","Error messages from schema validation may be generic and not actionable for LLM clients","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2870987193741047,"quality":0.47,"ecosystem":0.55,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:38.364Z","last_commit":"2026-04-10T21:13:21Z"},"community":{"stars":194,"forks":54,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-themotionmachine-omnifocus-mcp","compare_url":"https://unfragile.ai/compare?artifact=mcp-themotionmachine-omnifocus-mcp"}},"signature":"u8MXdf/Y5AJpPWZG5b7hDhnQNZCvFOMq0CrK7CFf6Dq5/oaHumPTxceSXjZ1ce2MZmCuJNaJgLWcu76bWkeFDA==","signedAt":"2026-06-22T04:16:28.953Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-themotionmachine-omnifocus-mcp","artifact":"https://unfragile.ai/mcp-themotionmachine-omnifocus-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-themotionmachine-omnifocus-mcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}