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Workflow Orchestrator MCP Server

@jeanibarz

代码仓库

关于 Workflow Orchestrator MCP Server

暂无概览

基本信息

分类

推理

许可证

Unlicense license

运行时

python

传输方式

stdio

发布者

jeanibarz

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.

FAQ from Workflow Orchestrator MCP Server

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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