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Enforce structured AI workflows with dependencies, quality gates, and validated outputs.
This is an open-source MIT-licensed MCP server whose description indicates persistent work-item and workflow enforcement features, with no declared secrets or remote endpoints. Based on the available material, overall risk appears relatively low, but it still warrants caution due to local execution capability and missing README/maintenance details.
The material explicitly states that no keys or environment variables are required, and it does not request third-party accounts, API tokens, or other sensitive credentials, so credential exposure appears limited.
The material declares no remote endpoints, and the description does not state that user data is sent to external services. Based on the available information, there is no explicit egress path, though the full implementation should still be verified in source.
System checks indicate the tool can execute code or start local processes. This is a normal high-privilege characteristic for MCP tools, so its actual system capabilities and runtime account permissions should be reviewed. The current material does not show abnormal privilege escalation beyond its stated purpose.
The description mentions 'persistent work items,' 'dependency graphs,' and 'actor attribution,' indicating likely local persistence of task-related data. The material does not specify read/write scope, storage location, or whether it accesses resources outside the workspace, so local data persistence and least-privilege settings should be reviewed.
The source is an open GitHub repository with an MIT license and about 193 stars, which are positive indicators that reduce risk and make the code auditable in principle. At the same time, the missing README and unknown maintenance status reduce visibility into implementation details and dependency posture, so the supply-chain dimension warrants caution rather than a high-risk rating.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "task-orchestrator" yet — see the docs or source repo.
Use task-orchestrator to create a software development workflow with four persistent work items: requirements analysis, task breakdown, code implementation, and test validation. Set dependencies as analysis → breakdown → implementation → testing. Define required output schemas for each stage and add a quality gate so failed tests block progression.
A created workflow structure with task dependency graph, stage schemas, and quality gate settings.
Use task-orchestrator to set up a task for a document-generation agent. Require it to output JSON with three fields: summary, key risks, and next-step recommendations. If any field is missing or typed incorrectly, block submission and return the validation failure reason.
A task definition with output schema, validation rules, and an example blocked result for invalid submissions.
Use task-orchestrator to build a research project workflow that records attribution for each AI agent or human participant across data collection, analysis, and conclusion writing. Require every work item to retain actor identity, completion time, deliverables, and traceable dependency sources.
A workflow configuration with attribution tracking, showing each task’s actor, deliverable, and upstream/downstream relationships.
Orchestrate complex AI agent workflows with dependency handling, retries, and progress tracking.
Run dependent or parallel agent tasks and return structured results in one call.
Orchestrate multiple AI agents to automate complex workflows efficiently.
Orchestrate role-based workflows and sync team tasks with Trello.
Coordinate multiple AI agents in parallel for debate, review, and synthesis.
Orchestrate local multi-agent workflows with gated lifecycle, handoffs, and host continuation.