orchestrator-mcp
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
orchestrator-mcp
An MCP server that runs a DAG of coding tasks across a pool of model workers, and believes only an exit code.
Why
Cheap workers are easy to hand work to and hard to trust. A worker will report "done" on code that does not run, rewrite the test that grades it, or echo a secret it saw in context. This server makes those failures structurally impossible instead of unlikely.
Related MCP server: JEVULON VII (JVII)
How it works
Plan as a graph. orchestrator_submit_plan validates the whole DAG first, so a bad plan creates nothing. Independent tasks run in parallel and dependent tasks receive upstream answers.
Blind acceptance. Each task carries an argv acceptance command. The harness pins it, runs it, and treats exit code 0 as the only success. The model cannot edit the script that grades it.
Safe by construction. Task claims and account leases are atomic. A hard-killed runner is taken over and the session still completes. Apply is scope-checked and hash-checked, so a file you edited meanwhile is reported, never overwritten. Secret-shaped output, binary output, path escapes and Windows device names are refused.
Results
103 offline tests pass. Live, 44 of 46 matrix tasks and 16 of 16 installed-config tasks pass on fifteen accounts. Against Sonnet 5.5 working alone, the orchestrated run used about half the net Claude tokens and 21% less time with equal code quality. Prose clarity was lower, and two of sixty worker quotes were invented and caught by the mechanical check.
Every table, the three security holes and the one real bug the tests found are in RESULTS.md. Raw data and scorers are in evidence/.
Quick start
python -m pip install -r requirements.txt
python -m unittest discover -s . -p "test_*.py"The suite is fully offline. Register server.py in your MCP client config. Real model calls stay off until you opt in with ORCHESTRATOR_GEMINI_ENABLED=1.
Tools
Plan level: orchestrator_submit_plan, orchestrator_wait, orchestrator_get_result, orchestrator_apply_session.
Step level: orchestrator_init, orchestrator_add_task, orchestrator_configure_task, orchestrator_run_session, orchestrator_get_status, orchestrator_apply_task, orchestrator_cancel_session, orchestrator_reset_session.
Pool: orchestrator_refresh_quotas, orchestrator_pool_status, orchestrator_list_sessions.
Configuration
Variable | Default |
|
|
|
|
|
|
|
|
|
|
| provider client string, see SECURITY.md |
|
|
| unset, model calls off |
Limits
Task workspaces are temporary copies, not an OS sandbox. Acceptance commands and generated code run with your user rights. Workers see only the files you list. Cancelling an in-flight HTTP call can take up to 120 seconds.
Licensed under the Apache License 2.0. Read SECURITY.md before running it against any provider account.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
This server cannot be deployed
Maintenance
Related MCP Connectors
Task tracking built for coding agents. Work is leased, so two agents never take the same SubTask.
Task manager for AI agents. Orchestrate agents to achieve large goals in unified control plane.
Shared task queue for humans and AI agents: leases, handoffs, approvals and signed receipts.
AI work orchestration for plans, tasks, teams, and coding-agent dispatch.
Related MCP Servers
- FlicenseBqualityBmaintenanceEnables AI coding assistants to run a machine-verified DESIGN→PLAN→EXECUTE→VERIFY→COMPLETE workflow with human approval gates, state integrity checks, and DAG task scheduling.7-
- AlicenseAqualityCmaintenanceEnables parallel AI coding agents to claim files atomically, detect and prevent workspace collisions, and receive deterministic zero-latency safety verdicts on destructive shell commands without any network egress. It also provides autonomous wave dispatch, worker recovery, and completion verification for multi-agent workflows via a shared coordination board.76 npmMIT
- AlicenseNot gradedqualityBmaintenanceEnables AI workers to run local, evidence-driven pull workflows with a shared task/run/event history, lease fencing, bounded permissions, and source-bound machine proof, while callers supply the authorized running worker.MIT
- AlicenseNot gradedqualityBmaintenanceTurns an MCP-capable planner such as Codex into a supervisor over pluggable worker backends, letting it run task-DAG workflows in isolated git worktrees with budgets, roles, acceptance tracking, metrics, and durable state that survives restarts.Apache 2.0