Workflow Orchestrator MCP Server

Name: Workflow Orchestrator MCP Server
Author: jeanibarz

@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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