TrancheVest
  • Introduction
  • 🔎System Architecture Overview
  • 🧱Risk Assessment Agent Framework
    • 💵Risk Assessment Layer
      • 💽Risk Profiling
      • 👾Risk Agents
      • Risk Assessment API
  • 💹Investment Tranche System
    • 🟢Green Tranche
    • 🔵Blue Tranche
    • ⚪Growth Tranche
    • Investment API
  • 💰Staking Pools
    • 🧩 Step 1: Connect Your Wallet
    • 💸 Step 2: Grab Your Test Tokens
    • 🧮 Step 3: Stake Your Tokens
    • 📈 Rewards Dashboard & Dynamic APYs
  • Agent Orchestration Layer (GAME)
    • GAME Framework
    • Agent Hierarchy
    • Decision-Making Logic
  • Financial Layer (GOAT SDK)
    • Investment Tranches
    • Transaction Management
    • Performance Analysis
  • 🦾MCP: Model Context Protocol
    • MCP Functionality
    • MCP Implementation
    • MCP Benefits
  • ACP: Economic Transactions Between Agents
    • ACP Process
    • ACP Use Cases
    • On-chain Records
  • Wallet Infra Layer (Crossmint)
    • Non-custodial Architecture
    • Wallet Security
    • Transaction Signing
  • Integration Layer
    • Smart Contract Interface
    • External Service Connectors
  • 🔐Security Considerations
    • Wallet Security Details
    • User Data Protection
    • Investment Security
  • Monitoring and Maintenance
    • Performance Monitoring
    • Maintenance Procedures
  • Development Roadmap
  • Frequently Asked Questions
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MCP: Model Context Protocol

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Last updated 26 days ago

TrancheVest MCP is a serves as a standardized context layer between the TrancheVest System and Large Language Models, featuring four key functionalities: Context Preservation maintains conversation history across interactions, Memory Management stores user preferences and trading history, Decision Support structures natural language requests for decision-making, and Market Analysis facilitates understanding of market conditions. The MCP enables four primary use cases within the system: Trading Strategy Decision-Making for strategy formulation, Risk Assessment for evaluating investment risks, Market Opportunity Identification for detecting potential opportunities, and User Preference Management for capturing trading preferences all working together to create a seamless bridge between users, the TrancheVest platform, and AI language models.

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