{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-task-orchestrator","slug":"task-orchestrator","name":"Task Orchestrator","type":"mcp","url":"https://github.com/EchoingVesper/mcp-task-orchestrator","page_url":"https://unfragile.ai/task-orchestrator","categories":["mcp-servers","code-editors","app-builders","automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-task-orchestrator__cap_0","uri":"capability://automation.workflow.workspace.aware.session.initialization.with.automatic.project.detection","name":"workspace-aware session initialization with automatic project detection","description":"Initializes MCP sessions by automatically detecting project workspaces and loading persistent state from SQLite database, enabling AI assistants to resume work across multiple sessions without manual context re-entry. The system scans the filesystem for project markers, reconstructs task history from the database, and establishes role-specific context for specialist agents based on workspace configuration.","intents":["Start a new task orchestration session that remembers previous work in this project","Resume an interrupted workflow without losing task state or progress","Automatically detect the current project structure and load relevant context"],"best_for":["Teams managing multi-session projects in Claude Desktop, Cursor IDE, or Windsurf","Developers building complex features that span multiple AI interaction sessions","Organizations needing persistent task state across different IDE integrations"],"limitations":["Workspace detection relies on filesystem markers — may fail in containerized or remote environments without proper path mapping","Session state is tied to local SQLite database — no built-in cloud sync or multi-machine state sharing","Automatic detection may conflict with monorepo structures containing multiple nested projects"],"requires":["Python 3.8+","MCP client application (Claude Desktop, Cursor IDE, Windsurf, or VS Code with MCP extension)","Write access to local filesystem for workspace detection and database creation","SQLite 3.x (typically bundled with Python)"],"input_types":["workspace path (string)","session configuration (JSON object with optional role definitions)"],"output_types":["session ID (UUID string)","workspace metadata (JSON with detected project structure)","task history summary (array of previous tasks with status)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-task-orchestrator__cap_1","uri":"capability://planning.reasoning.intelligent.task.decomposition.with.specialist.role.assignment","name":"intelligent task decomposition with specialist role assignment","description":"Breaks down complex user requests into structured subtasks by analyzing task semantics and assigning specialized agent roles (e.g., architect, developer, reviewer) based on task type and project context. Uses a planning engine that generates task dependency graphs and role-specific prompts, enabling each specialist to operate with focused context rather than generic instructions.","intents":["Split a large feature request into smaller, manageable subtasks with clear ownership","Automatically assign the right specialist (architect, developer, tester) to each subtask","Generate task dependency graphs to identify parallelizable work"],"best_for":["Engineering teams building features that require multiple skill domains (design, implementation, testing)","Solo developers managing complex projects who need AI to act as a project manager","Organizations adopting AI-assisted development workflows with role-based task allocation"],"limitations":["Task decomposition quality depends on initial request clarity — ambiguous requirements may produce suboptimal task graphs","Specialist role system is predefined and not dynamically learnable from project history","No built-in feedback loop to refine task decomposition based on subtask execution results"],"requires":["Python 3.8+","Active MCP session (initialized via orchestrator_initialize_session)","Task description (string, minimum 20 characters recommended for meaningful decomposition)","Optional: project configuration file defining available specialist roles"],"input_types":["task description (string)","project context (JSON with codebase structure, tech stack, constraints)","specialist role definitions (JSON array with role names and capabilities)"],"output_types":["task plan (JSON with subtasks, dependencies, and assigned roles)","task graph (DAG structure showing parallelizable work)","role-specific prompts (strings tailored for each specialist agent)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-task-orchestrator__cap_10","uri":"capability://memory.knowledge.task.artifact.storage.and.retrieval.with.metadata.indexing","name":"task artifact storage and retrieval with metadata indexing","description":"Stores task artifacts (code snippets, design documents, test results, etc.) alongside task metadata in the SQLite database with automatic indexing and retrieval capabilities. Artifacts are associated with their parent tasks and subtasks, enabling full traceability of what was produced during each phase of work.","intents":["Store code generated by developer specialists alongside task metadata","Retrieve artifacts from previous tasks to understand design decisions and implementation details","Build a searchable archive of project work for retrospectives and knowledge sharing"],"best_for":["Projects requiring audit trails of AI-generated work","Teams conducting retrospectives and learning from past task executions","Organizations building knowledge bases from task artifacts"],"limitations":["Artifact storage is limited to text-based content — binary artifacts (images, compiled code) require external storage","Artifact indexing is basic (task ID, role, timestamp) — no full-text search or semantic indexing","Large artifacts may impact database performance — no built-in compression or archival"],"requires":["Python 3.8+","SQLite database with write access","Sufficient disk space for artifact storage (typically <1GB per 1000 tasks with average 100KB artifacts)"],"input_types":["artifact content (string, code, or structured data)","artifact metadata (JSON with type, role, task ID)"],"output_types":["artifact record (JSON with ID, metadata, content)","artifact list (array of artifacts matching query criteria)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-task-orchestrator__cap_2","uri":"capability://automation.workflow.specialist.driven.subtask.execution.with.role.specific.context.injection","name":"specialist-driven subtask execution with role-specific context injection","description":"Executes individual subtasks by injecting role-specific context and constraints into the execution environment, allowing specialist agents to operate with focused information relevant to their assigned role. The system maintains a specialist registry that maps roles to context templates, execution constraints, and success criteria, enabling consistent behavior across multiple subtask executions.","intents":["Execute a subtask with context tailored to the assigned specialist role","Provide role-specific constraints (e.g., code style for developers, security focus for reviewers)","Track execution progress and capture role-specific artifacts (code, design docs, test results)"],"best_for":["Multi-agent AI workflows where different roles need different execution contexts","Teams standardizing how different specialist agents approach their assigned work","Projects requiring consistent output format and quality standards across specialist roles"],"limitations":["Specialist roles are statically defined at configuration time — cannot dynamically create new roles during execution","Context injection adds latency (~50-200ms per subtask) due to prompt construction and role lookup","No built-in mechanism to handle role conflicts or reassign subtasks if a specialist fails"],"requires":["Python 3.8+","Active task plan with assigned roles (from orchestrator_plan_task)","Specialist role definitions in configuration (JSON format)","Subtask ID and parent task context"],"input_types":["subtask ID (string/UUID)","assigned role (string, e.g., 'architect', 'developer', 'reviewer')","task context (JSON with project state, dependencies, constraints)","execution parameters (JSON with role-specific options)"],"output_types":["execution result (JSON with status, output, and artifacts)","role-specific artifacts (code snippets, design decisions, test reports)","execution metadata (duration, resource usage, role-specific metrics)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-task-orchestrator__cap_3","uri":"capability://memory.knowledge.persistent.task.state.management.with.sqlite.backed.database","name":"persistent task state management with sqlite-backed database","description":"Maintains complete task lifecycle state (planning, execution, completion) in a SQLite database with automatic schema migration, enabling task state to survive process restarts and be queried across sessions. The system implements a generic task model that stores task metadata, subtask relationships, execution results, and artifacts, with automatic schema versioning to support evolving data structures.","intents":["Persist task state so workflows can resume after interruption or IDE restart","Query historical task data to understand project progress and patterns","Store task artifacts (code, documents, test results) alongside task metadata"],"best_for":["Long-running projects spanning multiple sessions or days","Teams needing audit trails and historical task data for retrospectives","Organizations integrating task orchestration with project management systems"],"limitations":["SQLite is single-machine only — no built-in replication or multi-user concurrency beyond file-level locking","Database schema migrations are automatic but may cause performance issues on large datasets during version upgrades","No built-in backup or disaster recovery — relies on filesystem backups"],"requires":["Python 3.8+","SQLite 3.x (bundled with Python)","Write access to local filesystem for database file creation","Sufficient disk space for task history (typically <100MB for 1000+ tasks)"],"input_types":["task object (JSON with task metadata, subtasks, execution results)","artifact data (strings, code snippets, structured results)"],"output_types":["task record (JSON with database ID, timestamps, status)","task history (array of task records with filtering/sorting)","artifact retrieval (stored artifacts with metadata)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-task-orchestrator__cap_4","uri":"capability://data.processing.analysis.multi.step.task.result.synthesis.with.artifact.aggregation","name":"multi-step task result synthesis with artifact aggregation","description":"Combines results from multiple completed subtasks into a cohesive final output by aggregating role-specific artifacts, resolving conflicts between specialist outputs, and generating a unified summary. The synthesis engine analyzes task dependencies, merges artifacts in dependency order, and produces a final deliverable that integrates work from all specialists.","intents":["Combine code from multiple developers into a coherent implementation","Merge design decisions from architects with implementation details from developers","Generate a final project summary that incorporates all specialist contributions"],"best_for":["Complex projects requiring coordination between multiple specialist agents","Teams needing unified deliverables that integrate work from different roles","Workflows where subtask outputs must be merged in a specific order (e.g., design → implementation → testing)"],"limitations":["Conflict resolution between specialist outputs is rule-based and may not handle complex semantic conflicts","Synthesis quality depends on subtask artifact structure — poorly formatted outputs may fail to merge","No built-in rollback mechanism if synthesis fails — requires manual intervention to fix conflicting artifacts"],"requires":["Python 3.8+","Completed subtasks with artifacts (from orchestrator_complete_subtask)","Task dependency graph (from orchestrator_plan_task)","Synthesis configuration (JSON with merge rules and conflict resolution strategy)"],"input_types":["completed subtask results (array of JSON objects with artifacts)","task dependency graph (DAG structure)","synthesis rules (JSON with merge strategy and conflict resolution)"],"output_types":["synthesized result (unified JSON object with merged artifacts)","final deliverable (code, document, or structured output)","synthesis report (JSON with merge operations, conflicts resolved, and warnings)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-task-orchestrator__cap_5","uri":"capability://automation.workflow.workflow.progress.tracking.and.status.querying.across.sessions","name":"workflow progress tracking and status querying across sessions","description":"Provides real-time visibility into task orchestration progress by querying task state from the persistent database and computing workflow metrics (completion percentage, blocked tasks, critical path). The status system tracks task lifecycle transitions (planned → executing → completed) and identifies bottlenecks or failed subtasks that require intervention.","intents":["Check the current status of a running workflow without interrupting execution","Identify blocked tasks or failed subtasks that need manual intervention","Understand overall project progress and estimated completion time"],"best_for":["Long-running workflows where developers need visibility without constant polling","Teams managing multiple concurrent task orchestrations","Projects requiring status dashboards or integration with project management tools"],"limitations":["Status queries are point-in-time snapshots — no built-in streaming or real-time updates","Critical path analysis assumes task durations are known, which may not be accurate for AI-driven tasks","No built-in alerting for blocked tasks — requires external monitoring to detect failures"],"requires":["Python 3.8+","Active task orchestration session","Task ID or session ID to query status"],"input_types":["task ID or session ID (string/UUID)","status filter (optional, e.g., 'pending', 'executing', 'completed', 'failed')"],"output_types":["task status (JSON with current state, progress percentage, timestamps)","subtask list (array of subtasks with individual status)","workflow metrics (completion %, blocked count, critical path)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-task-orchestrator__cap_6","uri":"capability://tool.use.integration.mcp.protocol.server.implementation.with.seven.core.tools","name":"mcp protocol server implementation with seven core tools","description":"Implements the Model Context Protocol (MCP) specification as a server that exposes seven core tools (initialize_session, plan_task, execute_subtask, complete_subtask, synthesize_results, get_status, maintenance_coordinator) through a standardized interface compatible with Claude Desktop, Cursor IDE, Windsurf, and VS Code. The server handles tool invocation, parameter validation, error handling with timeouts, and both synchronous and asynchronous execution paths.","intents":["Integrate task orchestration capabilities into MCP-compatible AI clients","Expose orchestration functions as callable tools within Claude, Cursor, or other MCP clients","Enable AI assistants to manage complex workflows through a standardized protocol"],"best_for":["Developers building AI-assisted workflows in Claude Desktop, Cursor IDE, or Windsurf","Organizations standardizing on MCP for AI tool integration","Teams needing a protocol-compliant server for task orchestration"],"limitations":["MCP protocol overhead adds ~100-300ms latency per tool invocation due to serialization and deserialization","Error handling relies on timeout mechanisms which may not gracefully handle long-running subtasks","Tool parameter validation is schema-based and may reject valid inputs with complex nested structures"],"requires":["Python 3.8+","MCP client application (Claude Desktop, Cursor IDE, Windsurf, or VS Code with MCP extension)","MCP server configuration in client (typically JSON in config file)"],"input_types":["MCP tool invocation (JSON-RPC format with tool name and parameters)"],"output_types":["MCP tool response (JSON-RPC result with tool output or error)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-task-orchestrator__cap_7","uri":"capability://automation.workflow.automated.system.maintenance.and.health.monitoring","name":"automated system maintenance and health monitoring","description":"Provides automated maintenance operations including database cleanup, orphaned task detection, and health status reporting through the maintenance_coordinator tool. The system monitors server health, detects stale sessions, and performs preventive maintenance tasks to ensure long-term reliability without manual intervention.","intents":["Automatically clean up stale sessions and orphaned tasks from the database","Monitor server health and detect issues before they impact workflows","Perform preventive maintenance to keep the system running smoothly"],"best_for":["Long-running deployments where manual maintenance is impractical","Teams managing multiple concurrent task orchestrations over weeks or months","Organizations needing reliable, self-healing orchestration infrastructure"],"limitations":["Maintenance operations are scheduled but not truly asynchronous — they may block other operations during cleanup","Health monitoring is basic (server responsiveness, database connectivity) and doesn't detect logical errors in task execution","Orphaned task detection relies on timeout heuristics which may incorrectly flag slow-running tasks as stale"],"requires":["Python 3.8+","Active MCP session","Write access to SQLite database for cleanup operations"],"input_types":["maintenance operation type (string: 'cleanup', 'health_check', 'optimize')","optional parameters (JSON with cleanup thresholds, health check depth)"],"output_types":["maintenance report (JSON with operations performed, items cleaned, health status)","health metrics (server uptime, database size, session count)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-task-orchestrator__cap_8","uri":"capability://tool.use.integration.cross.ide.session.portability.with.standardized.mcp.interface","name":"cross-ide session portability with standardized mcp interface","description":"Enables task orchestration sessions to be portable across multiple IDE environments (Claude Desktop, Cursor IDE, Windsurf, VS Code) by implementing a standardized MCP interface that abstracts IDE-specific details. Sessions initialized in one IDE can be resumed in another by connecting to the same MCP server instance, with all task state and artifacts preserved.","intents":["Start a task in Claude Desktop and resume it in Cursor IDE without losing context","Switch between different AI clients while maintaining the same task orchestration session","Use the best tool for each phase of work (e.g., Claude for planning, Cursor for coding)"],"best_for":["Teams using multiple AI-assisted development tools and needing unified workflows","Developers who switch between Claude Desktop and Cursor IDE during a project","Organizations standardizing on MCP for cross-tool integration"],"limitations":["IDE-specific features (e.g., Cursor's code indexing) are not automatically shared across IDEs — each client maintains its own context","Session portability requires the same MCP server instance to be accessible from all IDEs, which may not be possible in air-gapped environments","Some IDE-specific optimizations (e.g., Windsurf's workspace awareness) may not transfer to other clients"],"requires":["Python 3.8+","MCP server instance running and accessible from all IDEs","MCP client configuration in each IDE pointing to the same server","Network connectivity between IDEs and server (for remote setups)"],"input_types":["session ID (string/UUID, same across all IDEs)"],"output_types":["session context (JSON with task state, artifacts, specialist roles)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-task-orchestrator__cap_9","uri":"capability://automation.workflow.role.based.context.templates.with.customizable.specialist.definitions","name":"role-based context templates with customizable specialist definitions","description":"Provides a configuration system for defining specialist roles with associated context templates, execution constraints, and success criteria that are applied during subtask execution. Roles are defined in configuration files and can be customized per project, enabling teams to standardize how different specialists (architect, developer, reviewer, etc.) approach their assigned work.","intents":["Define custom specialist roles tailored to project needs (e.g., 'security-reviewer', 'performance-engineer')","Standardize how each role approaches its work through context templates and constraints","Reuse role definitions across multiple projects"],"best_for":["Teams with standardized development processes that want to encode them in role definitions","Organizations building domain-specific task orchestration (e.g., security-focused, performance-focused)","Projects requiring consistent output format and quality standards across specialist roles"],"limitations":["Role definitions are static and loaded at server startup — changes require server restart","No built-in validation of role definitions — malformed templates may cause execution failures","Context templates are text-based and may not support complex conditional logic"],"requires":["Python 3.8+","Configuration file with role definitions (JSON or YAML format)","Understanding of template syntax for context injection"],"input_types":["role definition (JSON with name, context template, constraints, success criteria)"],"output_types":["role registry (JSON with all defined roles and their properties)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","MCP client application (Claude Desktop, Cursor IDE, Windsurf, or VS Code with MCP extension)","Write access to local filesystem for workspace detection and database creation","SQLite 3.x (typically bundled with Python)","Active MCP session (initialized via orchestrator_initialize_session)","Task description (string, minimum 20 characters recommended for meaningful decomposition)","Optional: project configuration file defining available specialist roles","SQLite database with write access","Sufficient disk space for artifact storage (typically <1GB per 1000 tasks with average 100KB artifacts)","Active task plan with assigned roles (from orchestrator_plan_task)"],"failure_modes":["Workspace detection relies on filesystem markers — may fail in containerized or remote environments without proper path mapping","Session state is tied to local SQLite database — no built-in cloud sync or multi-machine state sharing","Automatic detection may conflict with monorepo structures containing multiple nested projects","Task decomposition quality depends on initial request clarity — ambiguous requirements may produce suboptimal task graphs","Specialist role system is predefined and not dynamically learnable from project history","No built-in feedback loop to refine task decomposition based on subtask execution results","Artifact storage is limited to text-based content — binary artifacts (images, compiled code) require external storage","Artifact indexing is basic (task ID, role, timestamp) — no full-text search or semantic indexing","Large artifacts may impact database performance — no built-in compression or archival","Specialist roles are statically defined at configuration time — cannot dynamically create new roles during execution","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.6,"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":"2026-06-17T09:51:04.050Z","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=task-orchestrator","compare_url":"https://unfragile.ai/compare?artifact=task-orchestrator"}},"signature":"TSyGfdIqN5fJ+Ir77CJ0E0ii5A+OTuED4AQ5eQaSg1HGgk0rrd4j2ms9pyVu4C512UIkfsXuS5wfBPdgty+HCA==","signedAt":"2026-06-22T09:09:53.908Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/task-orchestrator","artifact":"https://unfragile.ai/task-orchestrator","verify":"https://unfragile.ai/api/v1/verify?slug=task-orchestrator","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"}}