ScriptFlow MCP Server

ScriptFlow turns the chaotic, trial‑and‑error style of interacting with AI assistants into a disciplined, repeatable automation framework. Instead of re‑typing the same prompts and waiting for the model to generate a solution each time, developers can capture successful exchanges as executable scripts. These scripts live in a shared repository that supports version control, metadata tagging, and team collaboration—making it possible to standardize AI‑driven workflows across an organization.

At its core, ScriptFlow provides a lightweight script library that can be queried and executed through the Model Context Protocol. Scripts are stored as plain files (Bash, Python, JavaScript, or TypeScript) accompanied by JSON metadata that records the name, description, language, and tags. This dual‑file approach keeps scripts human‑readable while enabling the MCP server to index and retrieve them efficiently. When a script is run, the server spawns the appropriate interpreter and passes any arguments supplied by the AI client, returning stdout, stderr, and exit codes back to the assistant. Because scripts are deterministic once written, they eliminate the token‑draining uncertainty of prompting a language model for each task.

Key capabilities include:

• Script lifecycle management: Create, edit, delete, and list scripts with rich metadata.
• Search & filtering: Find scripts by name, description, or tags, enabling quick discovery of reusable patterns.
• Execution with arguments: Run any script from the assistant’s context, passing dynamic parameters to customize behavior.
• Multi‑language support: Choose the scripting language that best fits your stack—Bash for shell tasks, Python for data processing, or JavaScript/TypeScript for web‑related automation.
• Team collaboration: Share proven workflows by simply adding scripts to the shared directory; all collaborators can access and reuse them.

Typical use cases include automating deployment pipelines, generating reports from data sources, or orchestrating complex cloud operations that require a sequence of API calls. In practice, a developer can ask an AI assistant to “set up a CI pipeline,” receive a script that configures the necessary files, and then execute it with a single command. The assistant can later invoke the same script whenever new projects are created, guaranteeing identical results without re‑prompting.

By integrating seamlessly with MCP clients such as Claude Desktop or any tool that speaks the protocol, ScriptFlow becomes a first‑class citizen in AI workflows. It bridges the gap between conversational AI and reliable, versioned automation, giving teams confidence that their scripts will run consistently while still benefiting from the creativity and flexibility of language models.