mcp-graph-loop
Related Servers
Alternatives to mcp-graph-loop
No user-submitted related servers found.
Related 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-
- AlicenseNot gradedqualityBmaintenanceEnables pipeline-driven task management for AI coding agents, with stage-gated workflows, dependency tracking, artifact versioning, and multi-agent collaboration.773 npm22MIT
- AlicenseAqualityBmaintenanceEnables coding agents to run persistent, budgeted work sessions with dependency graphs, human decision queues, required validation, and clean handoffs for autonomous or human-in-the-loop execution.16MIT
- AlicenseAqualityAmaintenanceEnables orchestrating external AI agents to execute development tasks with asynchronous management, automated acceptance verification, failure rework loops, and optional visual baseline validation.1114 npmApache 2.0
- AlicenseNot gradedqualityDmaintenanceEnables LLMs to execute and validate autonomous multi-agent workflows with tools for workflow execution, output validation, and execution logging, plus resources and prompts for task decomposition and error recovery.MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI coding providers to run structured multi-agent workflows with specs, tracked tasks, handoffs, health gates, and audited repair mode.29 npmMIT
TDQS
Scored across 16 tools
Most tools have distinct purposes with clear descriptions. get_graph_state and get_phase_status both provide state information but differ in scope (overall graph vs phase-level). validate_task_loop and validate_parallel_tasks are similar but clearly distinguished by single vs parallel execution.
Tool names consistently use snake_case with a verb_noun pattern (get_graph_state, start_task, add_task_node). Minor variation in verb choice (init vs initialize, scaffold, switch) is still predictable and does not undermine the overall pattern.
At 16 tools, the set is slightly above the comfortable range but each tool serves a distinct function in the workflow graph lifecycle. The complexity of the domain justifies the count, and there is no redundancy.
The tool set covers graph initialization, task management (add/update/start/reset), validation (single/parallel), state inspection, persistence, and project switching. Missing an explicit delete_task_node or mark_completed is a minor gap, but the core workflow is covered and workarounds exist.