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.
Token-Slash is an MCP (Model Context Protocol) server that extends AI assistants — like Claude, Cursor, and any MCP-compatible client — with new, real-world capabilities. It is built and deployed on Nitrostack, the fastest way to build, deploy, and share MCP apps.
- Overview
- What is MCP?
- How MCP is Used in Token-Slash
- Features
- Live Demo
- Getting Started
- Connect to an MCP Client
- Deploy Your Own MCP App
- Explore More MCP Apps
- FAQ
- Keywords
- License
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.
The Model Context Protocol (MCP) is an open standard that lets AI assistants securely connect to external tools, data sources, and services. Instead of being limited to what it was trained on, an AI model can call MCP servers to fetch live data, run actions, and integrate with real systems.
This project is one such MCP server. Learn more about building and shipping MCP apps at nitrostack.ai.
Token-Slash utilizes the Model Context Protocol (MCP) via the NitroStack Framework to serve as a standardized, protocol-compliant AI optimization server. By implementing MCP, Token-Slash allows any MCP client (such as Claude Desktop, Cursor IDE, NitroChat, or custom LLM agents) to natively invoke prompt optimization, model recommendation, and token analysis without requiring custom API integration code.
- Streamable HTTP Transport (
/mcp): Exposes high-throughput JSON-RPC 2.0 streaming for web clients and cloud environments. - Server-Sent Events (
/sse): Enables real-time, bi-directional communication between AI clients and the Token-Slash server. - NitroStack
@McpAppEngine: Handles module dependency injection, tool schema validation, and lifecycle hooks natively.
Token-Slash exposes 7 standardized tools to connected MCP clients:
analyze_prompt: Executes the complete end-to-end TokenSlash pipeline, returning token metrics, task complexity, recommended model, optimized prompt structure, and cost savings analysis.rewrite_prompt: Algorithmic prompt refactoring engine that removes conversational filler tokens and applies strict XML structural tags (<context>,<instructions>,<output_format>).synthesize_report: Generates executive-level ROI summaries and 500k monthly volume cost savings projections.recommend_model: Runs the 4-Model Python ML Ensemble (PromptIQ Predictor) to evaluate satisfaction, latency, retry risk, and select the lowest-cost model with output parity.estimate_tokens: Computes exact BPE token counts and character ratios usingjs-tiktoken(cl100k_base).classify_complexity: Extracts 77 syntactic features and classifies task taxonomy and structural complexity (0–100 scale).analyze_history: Profiles multi-session user history to personalize model recommendations and domain tuning.
health://checks: Provides real-time health checks and status diagnostics for deployment monitoring.widget://examples: Delivers interactive UI widget templates for embedded NitroChat displays.
- 🔌 MCP-native — works with any MCP-compatible client (Claude, Cursor, and more)
- 🛠️ Tools, resources & prompts — exposes structured capabilities to AI agents
- ⚡ Deployed on Nitrostack — reliable, hosted, and instantly shareable
- 🔐 Secure by design — secrets stay in environment variables, never in code
- 🧩 Composable — combine with other MCP apps to build powerful AI workflows
🚀 Live MCP endpoint: https://token-slash-main-serve-fable-stars-amrita-university-coimbatore.app.nitrocloud.ai
Point your MCP client at the endpoint above to try it instantly. Prefer a hosted setup? Deploy your own in minutes on Nitrostack.
- Node.js 18+ (or your project runtime)
- An MCP-compatible client (Claude Desktop, Cursor, etc.)
git clone https://github.com/B-Sheikh/TokenSlash.git
cd token-slash
npm installCopy the example environment file and add your own values:
cp .env.example .envnpm run startAdd this server to your MCP client configuration. A typical entry looks like:
{
"mcpServers": {
"token-slash": {
"url": "https://token-slash-main-serve-fable-stars-amrita-university-coimbatore.app.nitrocloud.ai"
}
}
}Restart your client and the tools from this MCP server will be available to your AI assistant.
Want to build and ship an MCP server like this one? Nitrostack lets you create, deploy, and host MCP apps in minutes — no infrastructure to manage.
👉 Start building: https://nitrostack.ai
- 🌙 Discover and share MCP projects with the community on r/mcptothemoon
- 🧰 Browse a growing catalog of MCP apps on Nitrostack
An MCP server implements the Model Context Protocol to expose tools, resources, and prompts that AI assistants can call. It lets an AI model take real actions and access live data.
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.
Any MCP-compatible client, including Claude Desktop and Cursor. New clients are adding MCP support regularly.
Use Nitrostack to build, deploy, and host MCP apps without managing infrastructure.
Enterprise AI & Workplace Automation · Token-Slash · MCP · Model Context Protocol · MCP server · MCP app · AI tools · AI agents · LLM tools · Claude MCP · Nitrostack · deploy MCP server · build MCP app
MIT © 2026
Built with ❤️ using the Model Context Protocol on Nitrostack. Share your MCP app on r/mcptothemoon.