Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-graph-loop Set up a task DAG for my project and run validation loops until all tasks pass."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A specialized MCP (Model Context Protocol) server for graph-based task orchestration with automated validation and self-healing retry loops.
flowchart TD
subgraph AI["🤖 AI Agent (Claude / Cursor / IDE)"]
A1[1. Initialize Graph] --> A2[2. Query Ready Tasks]
A2 --> A3[3. Start Task & Write Code]
A3 --> A4[4. Call validate_task_loop]
end
subgraph MCP["⚙️ BRD Graph Loop MCP Server"]
M1[(State Management: nodes, dependencies, status)]
M2[Dependency Resolver & DAG Engine]
M3[Command Executor & Output Capture]
M4[Loop Controller: Retries, Max Attempts, Error Logging]
end
A1 -->|init_project_graph| M1
A2 -->|get_ready_tasks| M2
A3 -->|start_task| M1
A4 -->|validate_task_loop| M3
M3 -->|Pass: exitCode 0| M4
M3 -->|Fail: exitCode != 0| M4
M4 -->|Unlock Next Tasks| M2
M4 -->|Return Error Context| AIRelated MCP server: Bernstein - Multi-agent orchestration
Each task node moves through deterministic states based on its prerequisites and validation results:
stateDiagram-v2
[*] --> PENDING : Initial state with unresolved dependencies
PENDING --> READY : All 'depends_on' tasks reach COMPLETED
READY --> IN_PROGRESS : AI calls 'start_task'
state "Validation Loop" as Loop {
IN_PROGRESS --> VALIDATING : AI calls 'validate_task_loop'
VALIDATING --> RETRYING : Command fails (exitCode != 0 & attempts < max)
RETRYING --> IN_PROGRESS : AI reads error logs and fixes code
}
VALIDATING --> COMPLETED : Command passes (exitCode 0)
VALIDATING --> FAILED : Command fails & max_attempts exceeded
COMPLETED --> [*] : Unlocks downstream PENDING nodes
FAILED --> [*] : Can be reset with 'reset_task_node'Tasks have explicit dependencies (depends_on: ["task_a", "task_b"]). The server automatically ensures tasks only become READY when all their prerequisite tasks are COMPLETED.
Instead of hoping code works, each node specifies a validation_command (e.g., npm test, tsc --noEmit, pytest, eslint):
Pass (exitCode: 0): Loop status becomes PASSED, node becomes COMPLETED, and dependent nodes automatically switch to READY.
Fail (exitCode != 0): The server logs full stdout/stderr and exit codes in error_logs, increments current_attempt, and returns the error output to the AI.
Self-Correction: The AI analyzes the error, modifies code, and calls validate_task_loop again until it passes or hits max_attempts.
scaffold_project_docs)Before initializing the graph, the AI agent can generate standard project documentation (Architecture, Phase-wise Tasks, and Test Cases) based on the user's requirements:
{
"targetDirectory": "./",
"architectureContent": "# Project Architecture\n...",
"phaseTasks": [
{ "fileName": "PHASE_1.md", "content": "# Phase 1 Tasks\n..." }
],
"testCasesContent": "# Integration Tests\n..."
}init_project_graph)The AI agent creates a task graph for a project:
{
"projectName": "Auth Feature",
"projectRoot": "/path/to/your/project/dir",
"nodes": [
{
"id": "schema",
"title": "Define User Database Schema",
"description": "Create Prisma schema and migration scripts",
"depends_on": [],
"validation_command": "npx prisma validate",
"max_attempts": 3
},
{
"id": "jwt_service",
"title": "Build JWT Token Service",
"description": "Implement sign, verify, and refresh token functions",
"depends_on": ["schema"],
"validation_command": "npm run test -- jwt.test.ts",
"max_attempts": 3
},
{
"id": "login_route",
"title": "Build API Login Endpoint",
"description": "Express POST /api/login endpoint with validation",
"depends_on": ["jwt_service"],
"validation_command": "npm run test -- auth.test.ts",
"max_attempts": 3
}
]
}get_ready_tasks)The agent asks what to work on next:
{
"ready_count": 1,
"ready_tasks": [
{
"id": "schema",
"title": "Define User Database Schema",
"status": "READY"
}
]
}(Notice jwt_service and login_route remain PENDING because their dependencies aren't done yet).
start_task)The agent claims the task:
{ "nodeId": "schema" }Node status transitions to IN_PROGRESS.
validate_task_loop)After the agent writes the schema files, it triggers the validation loop:
{ "nodeId": "schema" }If it passes:
schema status becomes COMPLETED.
jwt_service automatically becomes READY!
If it fails:
MCP returns:
{
"validation_passed": false,
"message": "Validation failed on attempt 1/3. Node 'schema' is in RETRYING status.",
"result": {
"exitCode": 1,
"error": "Syntax error at line 14: invalid relation syntax"
}
}The AI reviews the error, fixes line 14, and re-calls validate_task_loop.
Add this to your MCP settings file (~/.cursor/mcp.json, Claude Desktop config, or .gemini/config/mcp_config.json):
{
"mcpServers": {
"brd-graph-loop": {
"command": "node",
"args": [
"/Volumes/DATA/html work/mcp-graph-loop-server/build/index.js"
]
}
}
}Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Build, validate, and deploy multi-agent AI solutions from any AI environment.
Verifies AI agent work end to end: real artifacts and outcomes checked, not self-reported success.
Durable agent-to-agent handoffs and shared scratchpad for multi-agent workflows.
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