Enterprise AI & Workplace Automation Fable StarsSubmitted July 26, 2026

Token-Slash

An MCP app on the Model Context Protocol built by Fable Stars at the Amrita University Coimbatore NitroStack × MCP To The Moon hackathon and deployed on NitroStack.

About this project

TokenSlash is an advanced solution for prompt optimization and model selection that uses Artificial Intelligence as its driving force, leading to considerably lower costs, reduced tokens consumption, and shorter time spans for Large Language Model software. It works as an intelligent layer between the user and the AI software, analyzing the prompts before it is deployed. The system calculates tokens consumed, determines the type and complexity of the task, eliminates redundant or unnecessary content, and reformulates the prompt into a comprehensible form. Thereafter TokenSlash compares a number of AI models against their anticipated output quality, price, time for execution, and retry probability, in order to provide the user with the best possible solution in terms of cost and effective performance. TokenSlash is advantageous for developers, startups, teams working with products, departments engaged in customer support, enterprises, and teams responsible for governance. It is helpful for chatbots, coding assistants, document processing systems, support automation tools, and other systems dealing with AI technologies on a daily basis. TokenSlash is created based on NitroStack SDK and Model Context Protocol and works with NitroCloud, NitroChat, Cursor, and Claude Desktop. So AI work becomes easier and more transparent.

Enterprise AI & Workplace Automation track

Develop AI agents and automation tools that improve productivity, streamline workflows, and enhance business operations.

Team Fable Stars

  • B Abhishek BharathiLead

  • Thanushree Sivakumar

  • Sanjay S M

  • Sathya K

Frequently asked questions

What does Token-Slash do?
TokenSlash is an advanced solution for prompt optimization and model selection that uses Artificial Intelligence as its driving force, leading to considerably lower costs, reduced tokens consumption, and shorter time spans for Large Language Model software. It works as an intelligent layer between the user and the AI software, analyzing the prompts before it is deployed. The system calculates tokens consumed, determines the type and complexity of the task, eliminates redundant or unnecessary content, and reformulates the prompt into a comprehensible form. Thereafter TokenSlash compares a number of AI models against their anticipated output quality, price, time for execution, and retry probability, in order to provide the user with the best possible solution in terms of cost and effective performance. TokenSlash is advantageous for developers, startups, teams working with products, departments engaged in customer support, enterprises, and teams responsible for governance. It is helpful for chatbots, coding assistants, document processing systems, support automation tools, and other systems dealing with AI technologies on a daily basis. TokenSlash is created based on NitroStack SDK and Model Context Protocol and works with NitroCloud, NitroChat, Cursor, and Claude Desktop. So AI work becomes easier and more transparent.
Who built Token-Slash?
Token-Slash was built by team Fable Stars at the Amrita University Coimbatore NitroStack × MCP To The Moon hackathon, in the Enterprise AI & Workplace Automation track.
What is an MCP app and how is it built?
An MCP app is an application built on the Model Context Protocol — an open standard that lets AI agents connect to tools, data, and APIs. This project exposes MCP tools and resources that agentic AI systems can call. It was built and deployed on NitroStack, the full-stack platform for shipping MCP apps and servers.