{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-dart","slug":"dart","name":"Dart","type":"mcp","url":"https://github.com/its-dart/dart-mcp-server","page_url":"https://unfragile.ai/dart","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-dart__cap_0","uri":"capability://tool.use.integration.mcp.based.task.crud.operations.with.real.time.sync","name":"mcp-based task crud operations with real-time sync","description":"Exposes Dart task management through the Model Context Protocol (MCP), enabling LLM agents to create, read, update, and delete tasks via standardized MCP resource handlers. Implements bidirectional sync between the MCP server and Dart's backend API, maintaining task state consistency through webhook-based change notifications and polling fallbacks. Uses JSON-RPC 2.0 message framing for stateless request/response handling across stdio, HTTP, or SSE transports.","intents":["I want my AI agent to programmatically create and update tasks in Dart without custom API client code","I need to sync task status changes between Dart and my LLM workflow in real-time","I want to query tasks by project, assignee, or status and feed results into agent decision-making"],"best_for":["AI agent developers building autonomous task management workflows","Teams integrating Dart with Claude, GPT, or open-source LLMs via MCP","Builders prototyping AI-native project management assistants"],"limitations":["MCP transport overhead adds ~50-200ms per request depending on stdio vs HTTP","No built-in batch operations — each task mutation requires separate RPC call","Webhook-based sync requires publicly routable callback URL; polling fallback may lag by 30-60 seconds","Task filtering limited to server-side predicates; complex client-side filtering requires fetching full task list"],"requires":["Dart workspace with API credentials (workspace ID + API token)","MCP client implementation (Claude Desktop, custom LLM framework, or MCP SDK)","Network connectivity to Dart backend (itsdart.com or self-hosted instance)","Node.js 16+ or Python 3.8+ to run the MCP server process"],"input_types":["JSON task objects with title, description, assignee, status, priority fields","Query filters as JSON (project_id, assignee_id, status enum, date ranges)","Task ID strings for read/update/delete operations"],"output_types":["JSON task objects with full metadata (id, created_at, updated_at, custom fields)","Task lists with pagination metadata","Operation confirmations with updated task state"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dart__cap_1","uri":"capability://search.retrieval.document.retrieval.and.embedding.aware.search.within.projects","name":"document retrieval and embedding-aware search within projects","description":"Enables MCP clients to query and retrieve documents attached to Dart projects, with support for semantic search via embeddings. The server indexes document metadata (title, content snippets, creation date, owner) and can filter by project scope. Implements vector similarity search if Dart's backend supports embedding storage, falling back to keyword/metadata filtering. Returns document objects with content references that LLM agents can use to ground task decisions or generate context-aware documentation.","intents":["I want my AI agent to search project documentation and use relevant docs as context when creating or updating tasks","I need to retrieve all documents in a project and embed them for semantic search across task descriptions","I want to find documents by keyword or semantic similarity and link them to tasks programmatically"],"best_for":["AI agents that need to ground task decisions in project documentation","Teams building knowledge-aware project management assistants","Builders implementing RAG (Retrieval-Augmented Generation) workflows over Dart projects"],"limitations":["Document content retrieval may be limited to summaries or snippets; full-text retrieval depends on Dart's storage model","Embedding-based search requires Dart backend support; if unavailable, falls back to keyword matching only","No full-text indexing on the MCP server side — all search queries hit Dart's backend API","Large document collections (>10k docs per project) may incur pagination overhead"],"requires":["Dart workspace with documents attached to projects","API credentials with read access to project documents","Optional: embedding model API key (OpenAI, Anthropic, or local) if semantic search is desired"],"input_types":["Project ID string to scope document queries","Search query strings (keyword or semantic)","Document ID strings for direct retrieval","Filter objects (date range, owner, document type)"],"output_types":["Document metadata objects (id, title, created_at, owner, project_id)","Document content snippets or full text","Similarity scores if embedding search is used","Paginated document lists"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dart__cap_2","uri":"capability://memory.knowledge.project.hierarchy.and.metadata.querying.with.team.context","name":"project hierarchy and metadata querying with team context","description":"Exposes Dart's project structure (projects, subprojects, team members, roles) as queryable MCP resources. The server fetches project metadata including name, description, owner, team members, and permission model, enabling agents to understand organizational context before creating or modifying tasks. Implements role-based filtering so agents only see/modify projects they have access to, enforced via Dart's backend permission checks. Returns hierarchical project structures that agents can traverse to find the right project scope for a task.","intents":["I want my AI agent to list all projects and subprojects in my workspace to determine where to create a new task","I need to query team members and their roles to assign tasks to the right person","I want my agent to understand project hierarchy and permissions before suggesting task assignments"],"best_for":["AI agents managing tasks across multi-project workspaces","Teams with complex organizational hierarchies (nested projects, multiple teams)","Builders implementing permission-aware task assignment workflows"],"limitations":["Project hierarchy depth is limited by Dart's data model; deeply nested projects (>5 levels) may require multiple queries","Team member list is static per query; real-time team changes (new members, role updates) require re-querying","Permission checks are enforced server-side; agents cannot predict permission errors before attempting operations","No caching of project metadata on the MCP server — each query hits Dart's backend"],"requires":["Dart workspace with multiple projects or team members","API credentials with read access to workspace structure","Appropriate permissions to view team members and project settings"],"input_types":["Project ID or name to query specific project","Workspace ID to list all projects","Optional filters (owner, team, status)"],"output_types":["Project objects with metadata (id, name, description, owner, created_at)","Hierarchical project trees with parent/child relationships","Team member lists with roles and permissions","Permission matrices (who can do what in each project)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dart__cap_3","uri":"capability://data.processing.analysis.custom.field.and.metadata.schema.introspection","name":"custom field and metadata schema introspection","description":"Allows MCP clients to query the schema of custom fields, task types, and metadata structures defined in a Dart workspace. The server introspects Dart's field definitions (name, type, validation rules, allowed values for enums) and returns them as structured JSON, enabling agents to understand what fields are available before creating or updating tasks. Implements schema caching to reduce API calls and supports dynamic schema updates via webhook notifications when workspace admins modify field definitions.","intents":["I want my AI agent to know what custom fields are available in my workspace before creating a task","I need to validate task data against the workspace schema before submitting it to Dart","I want my agent to populate custom fields intelligently based on field type and constraints"],"best_for":["Teams with heavily customized Dart workspaces (many custom fields or task types)","AI agents that need to generate valid task data without trial-and-error","Builders implementing schema-aware task creation workflows"],"limitations":["Schema introspection is read-only; agents cannot modify field definitions via MCP","Schema changes require webhook updates or manual re-query; no automatic schema refresh on every request","Complex validation rules (e.g., conditional field visibility) may not be fully expressible in the schema JSON","Enum values for dropdown fields must be pre-fetched; no lazy loading of large enum lists"],"requires":["Dart workspace with custom fields or task types defined","API credentials with read access to workspace settings","Optional: webhook endpoint if real-time schema updates are desired"],"input_types":["Workspace ID to fetch schema","Optional: specific field name to query individual field schema"],"output_types":["JSON schema objects describing field types (text, number, enum, date, etc.)","Validation rules (required, min/max length, regex patterns)","Enum values for dropdown/select fields","Field metadata (display name, description, default value)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dart__cap_4","uri":"capability://memory.knowledge.task.comment.and.activity.log.retrieval.with.context.threading","name":"task comment and activity log retrieval with context threading","description":"Enables MCP clients to fetch task comments, activity logs, and conversation threads associated with tasks. The server retrieves comment history with metadata (author, timestamp, edited status) and activity events (status changes, field updates, assignments). Implements threading to group related comments and supports filtering by date range or author. Returns comment objects with author information and timestamps, allowing agents to understand task discussion context and make informed decisions about task updates or escalations.","intents":["I want my AI agent to read task comments and understand the discussion context before updating a task","I need to fetch activity logs to see who changed what and when on a task","I want my agent to understand task history and use it to make smarter task management decisions"],"best_for":["AI agents that need to understand task discussion context before taking action","Teams using Dart for collaborative task management with rich comment threads","Builders implementing context-aware task automation (e.g., auto-escalation based on discussion)"],"limitations":["Comment retrieval is paginated; fetching large comment histories (>1000 comments) requires multiple queries","Activity logs may be truncated or summarized depending on Dart's retention policy","Comment editing history is not exposed; only current comment text and edit timestamp are available","No full-text search across comments; filtering is limited to author, date, or comment type"],"requires":["Dart workspace with tasks that have comments or activity","API credentials with read access to task comments and activity logs","Task ID to query comments for"],"input_types":["Task ID string","Optional filters (date range, author, comment type)","Pagination parameters (limit, offset)"],"output_types":["Comment objects with author, text, timestamp, and edit status","Activity log entries (status changes, field updates, assignments)","Threaded comment structures (parent/child relationships)","Pagination metadata (total count, next page token)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dart__cap_5","uri":"capability://automation.workflow.batch.task.operations.with.transaction.like.semantics","name":"batch task operations with transaction-like semantics","description":"Provides MCP endpoints for performing multiple task operations (create, update, delete) in a single request, with optional transaction-like semantics to ensure atomicity. The server batches operations and sends them to Dart's backend, rolling back all changes if any operation fails (if supported by Dart's API). Implements request deduplication to prevent duplicate operations if the MCP client retries. Returns results for each operation in the batch, allowing agents to handle partial failures gracefully.","intents":["I want my AI agent to create multiple related tasks in a single MCP call to reduce latency","I need to update a task and its subtasks atomically, rolling back if any update fails","I want my agent to perform bulk task operations without making N separate API calls"],"best_for":["AI agents performing multi-step task workflows (e.g., create task + create subtasks + assign)","Teams with high-volume task creation or bulk updates","Builders implementing efficient task automation with minimal API round-trips"],"limitations":["Batch size is limited (typically 10-100 operations per batch); very large batches require multiple requests","Transaction rollback is best-effort; Dart's backend may not support full ACID semantics","Partial failure handling requires agent-side logic; no automatic retry of failed operations","Batch operations may have higher latency than individual operations due to coordination overhead"],"requires":["Dart API that supports batch operations (check server version)","API credentials with write access to tasks","MCP client that supports batch request semantics"],"input_types":["Array of task operation objects (create, update, delete)","Optional: transaction flag to enable atomic semantics","Optional: idempotency key for deduplication"],"output_types":["Array of operation results (success/failure status)","Updated task objects for successful operations","Error details for failed operations","Transaction status (committed, rolled back, partial)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dart__cap_6","uri":"capability://planning.reasoning.ai.generated.task.suggestions.and.reasoning.context","name":"ai-generated task suggestions and reasoning context","description":"Exposes Dart's AI-native features through MCP, allowing agents to access AI-generated task suggestions, reasoning traces, and context attachments. The server retrieves suggestions generated by Dart's AI engine (e.g., auto-generated subtasks, priority recommendations) and returns them with reasoning metadata (why the suggestion was made, confidence score). Enables agents to inspect and act on AI-generated content, creating a feedback loop where agent decisions inform Dart's AI model. Implements versioning to track suggestion history and allow agents to compare different AI suggestions.","intents":["I want my AI agent to see Dart's AI-generated task suggestions and decide whether to accept or modify them","I need to understand the reasoning behind AI-generated subtasks or recommendations","I want my agent to provide feedback on AI suggestions to improve Dart's AI model over time"],"best_for":["Teams using Dart's AI features and wanting to integrate with external AI agents","Builders implementing AI-to-AI collaboration workflows (Dart AI + LLM agent)","Organizations building feedback loops to improve AI task management"],"limitations":["AI suggestions are generated asynchronously; agents may need to poll or wait for suggestions to be ready","Reasoning traces are opaque; agents cannot modify or override Dart's AI reasoning directly","Feedback mechanisms are limited to accept/reject; no fine-grained feedback on individual suggestion components","AI suggestion quality depends on Dart's training data and model; agents cannot customize the AI model via MCP"],"requires":["Dart workspace with AI features enabled","API credentials with read access to AI suggestions","Optional: feedback API endpoint if agent wants to provide suggestions feedback"],"input_types":["Task ID to fetch AI suggestions for","Optional: suggestion type filter (subtasks, priority, assignment, etc.)","Optional: feedback (accept, reject, modify)"],"output_types":["AI suggestion objects with content and metadata","Reasoning traces explaining why suggestions were made","Confidence scores for suggestions","Suggestion history and versions","Feedback confirmation"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dart__cap_7","uri":"capability://automation.workflow.batch.task.and.document.operations.with.error.handling","name":"batch task and document operations with error handling","description":"Supports bulk operations (create multiple tasks, update task statuses in batch, link docs to tasks) through MCP tool calls with transactional semantics and per-item error reporting. Implements batching logic that groups operations for efficiency while providing granular error feedback for failed items.","intents":["Create multiple tasks from sprint planning or backlog import","Bulk update task statuses based on workflow events","Link multiple documents to tasks in a single operation","Execute multi-step workflows with rollback on partial failures"],"best_for":["AI agents performing bulk task management","Builders creating import/migration tools","Teams automating sprint planning or backlog refinement"],"limitations":["No true transactions; partial failures are possible","Batch size limits depend on Dart backend; very large batches may be rejected","Error handling is per-item; no rollback mechanism","Rate limiting applies to batch operations; high-frequency bulk ops may be throttled"],"requires":["Dart workspace with batch operation support","MCP host supporting batch tool calls","API credentials with write scope"],"input_types":["Arrays of task/doc objects","Batch operation type (create, update, link)","Error handling strategy"],"output_types":["Batch result object with success/failure counts","Per-item error details","Created/updated entity IDs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Dart workspace with API credentials (workspace ID + API token)","MCP client implementation (Claude Desktop, custom LLM framework, or MCP SDK)","Network connectivity to Dart backend (itsdart.com or self-hosted instance)","Node.js 16+ or Python 3.8+ to run the MCP server process","Dart workspace with documents attached to projects","API credentials with read access to project documents","Optional: embedding model API key (OpenAI, Anthropic, or local) if semantic search is desired","Dart workspace with multiple projects or team members","API credentials with read access to workspace structure","Appropriate permissions to view team members and project settings"],"failure_modes":["MCP transport overhead adds ~50-200ms per request depending on stdio vs HTTP","No built-in batch operations — each task mutation requires separate RPC call","Webhook-based sync requires publicly routable callback URL; polling fallback may lag by 30-60 seconds","Task filtering limited to server-side predicates; complex client-side filtering requires fetching full task list","Document content retrieval may be limited to summaries or snippets; full-text retrieval depends on Dart's storage model","Embedding-based search requires Dart backend support; if unavailable, falls back to keyword matching only","No full-text indexing on the MCP server side — all search queries hit Dart's backend API","Large document collections (>10k docs per project) may incur pagination overhead","Project hierarchy depth is limited by Dart's data model; deeply nested projects (>5 levels) may require multiple queries","Team member list is static per query; real-time team changes (new members, role updates) require re-querying","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:03.037Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=dart","compare_url":"https://unfragile.ai/compare?artifact=dart"}},"signature":"iZpILuKodEvZH3DEKM6zJMBrYioR3gMR8epUu96QHkP2siDy/gc1uDXBfColhPzzIrz/P4e4k0eBjyS/V1zTDw==","signedAt":"2026-06-21T07:41:23.679Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dart","artifact":"https://unfragile.ai/dart","verify":"https://unfragile.ai/api/v1/verify?slug=dart","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"}}