Workflow Orchestrator MCP Server

@jeanibarz

Basic information

About Workflow Orchestrator MCP Server

No overview available yet

Workflow Orchestrator MCP Server config

Add this server to your MCP-compatible client using the configuration below.

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

Workflow Orchestrator MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

Frequently asked questions

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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