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Workflow Orchestrator MCP Server

@jeanibarz

リポジトリ

基本情報

カテゴリ

推論

ライセンス

Unlicense license

トランスポート

stdio

作者

jeanibarz

Workflow Orchestrator MCP Server について

概要はまだありません

Workflow Orchestrator MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "orchestrator-mcp-server": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

Workflow Orchestrator MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Workflow Orchestrator MCP Server?

An AI-powered MCP server that manages and executes complex, dynamic workflows using a Large Language Model for intelligent decision-making. It is designed for developers who need adaptable, non-linear workflows within MCP-compatible clients like Cline.

How to use Workflow Orchestrator MCP Server?

Install dependencies with uv sync, set required environment variables (WORKFLOW_DEFINITIONS_DIR, WORKFLOW_DB_PATH, GEMINI_MODEL_NAME), then run uv run python -m orchestrator_mcp_server or the orchestrator-mcp-server command. The server exposes MCP tools: list_workflows, start_workflow, get_workflow_status, advance_workflow, resume_workflow. Workflows are defined as Markdown files in the configured definitions directory.

Key features of Workflow Orchestrator MCP Server?

  • AI-driven decisions for dynamic branching and error handling.
  • Workflows and steps defined in human-readable Markdown.
  • Persistent state stored in a local SQLite database.
  • Resumption of interrupted workflows with state reconciliation.
  • Reusable, modular step definitions.
  • Human-readable and editable workflow definitions.

Use cases of Workflow Orchestrator MCP Server?

  • Automating multi-step code review and refactoring processes.
  • Managing long-running task pipelines that can be paused and resumed.
  • Creating adaptable GitLab issue analysis workflows.
  • Generating commit suggestions or jokes with dynamic AI steps.
  • Building custom orchestration logic for data processing or CI/CD.

よくある質問

1

How is the server configured?

Configuration is done via environment variables: `WORKFLOW_DEFINITIONS_DIR` (path to Markdown workflow files), `WORKFLOW_DB_PATH` (path to SQLite database), `GEMINI_MODEL_NAME` (e.g., `gemini-2.

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