kinetifi-mcp

kinetifi-mcp

Cross-chain MCP server that coordinates operations between Casper and Mantle networks, enabling yield scanning, portfolio aggregation, and intent-based strategy generation.

Category
Visit Server

README

KinetiFi Cross-Chain MCP Server (kinetifi-mcp)

A high-performance Model Context Protocol (MCP) server built with Python and FastMCP. This server coordinates cross-chain operations between the Casper Network and the Mantle Network, enabling agentic systems to scan yields, aggregate multi-chain portfolio balances, and generate intent-based execution strategies.


Features

  • Cross-Chain Yield Scanner: Connects to DefiLlama (for Mantle) and CSPR.cloud APIs (for Casper) to scan active DEX volumes, estimate APYs, and identify yield-generating pools.
  • Unified Portfolio Aggregator: Aggregates and standardizes balances for both Casper and Mantle accounts, parsing raw base units (motes/wei) into standardized, human-readable decimal strings.
  • Intent-Based Strategy Generator: Evaluates a user's natural language goal against their liquid holdings and active cross-chain yields to design optimal transaction paths (bridges, swaps, or stakes).
  • Stateless & Secure: Functions strictly as a read-only advisor. It does not handle private keys or execute transactions, adhering to KinetiFi's Zero-Trust design.

Repository Structure

kinetifi-mcp/
├── .ai_rules             # Local developer instructions
├── requirements.txt      # Python dependencies
├── server.py             # Server entrypoint and tool registration
├── models/               # Pydantic models defining standard schemas
│   ├── execution_intent.py   # Strategy intent blueprint schema
│   ├── portfolio_state.py    # Cross-chain asset balance schema
│   └── yield_state.py        # Cross-chain pool yield schema
├── tools/                # Sub-modules implementing individual query logic
│   ├── __init__.py
│   ├── generate_strategy.py  # Strategy formulation logic
│   ├── get_portfolio_state.py# Portfolio balancer logic
│   └── scan_yields.py        # DefiLlama and CSPR.cloud scanners
└── tests/                # Offline unit and integration tests
    ├── test_smoke.py         # Basic connection tests
    ├── test_phase2_yields.py # Yield scanner mock assertions
    ├── test_phase3_portfolio.py # Portfolio state mock tests
    └── test_phase4_strategy.py  # Intent strategy generator tests

Prerequisites

Ensure you have the following installed:

  • Python 3.10+ (verified on Python 3.14)
  • Virtual Environment Tool (venv)
  • MCP Host (e.g., Claude Desktop, KinetiFi Agent, or any custom client)

Installation & Setup

  1. Navigate to the Directory:

    cd /home/tmalone1250/KinetiFi_local/KinetiFi/kinetifi-mcp
    
  2. Create and Activate a Virtual Environment:

    python3 -m venv .venv
    source .venv/bin/activate
    
  3. Install Dependencies:

    pip install --upgrade pip
    pip install -r requirements.txt
    

Configuration

Set up your .env file in the project root to authenticate with Casper APIs:

# KinetiFi MCP Environment Variables
CSPR_CLOUD_BASE_URL=https://api.testnet.cspr.cloud
CSPR_CLOUD_API_KEY=your_cspr_cloud_api_key_here

Running the Server

Development Mode (with Live Reload Dashboard)

fastmcp dev server.py

Production Mode (stdio transport)

python server.py

MCP Tool Reference

kinetifi-mcp exposes 4 tools to clients:

1. get_status

Checks the server health.

  • Response Format: "KinetiFi MCP Server is operational."

2. scan_yields

Aggregates live yield opportunities across Casper DEXs (e.g., Friendly Market) and Mantle L2 DEXs (e.g., Agni, Merchant Moe).

  • Response Format: A JSON string representing a list of cross-chain pool yields:
    [
      {
        "network": "Mantle",
        "dex_name": "merchant-moe",
        "trading_pair": "WMNT-USDT",
        "apy": 45.2,
        "pool_hash": "0x365722f12ceb2063286a268B03c654Df81B7C00F"
      },
      {
        "network": "Casper",
        "dex_name": "Friendly Market",
        "trading_pair": "CSPR-USDC",
        "apy": 12.45,
        "pool_hash": "hash-abcd..."
      }
    ]
    

3. get_portfolio_state

Aggregates portfolio balances from Casper and Mantle addresses, converting them into standardized decimal representation.

  • Arguments:
    • casper_account (string, required): The Casper public key or account hash.
    • mantle_account (string, required): The Mantle EVM wallet address.
  • Response Format:
    {
      "balances": [
        {
          "network": "Casper",
          "asset_symbol": "CSPR",
          "balance": "1500.2500",
          "usd_value_estimate": null
        },
        {
          "network": "Mantle",
          "asset_symbol": "USDC",
          "balance": "250.00",
          "usd_value_estimate": null
        }
      ]
    }
    

4. generate_strategy

Analyzes the aggregated portfolio and yield conditions to design a non-binding execution plan that maximizes yields or balances risks.

  • Arguments:
    • user_goal (string, required): The user's goal (e.g. "Maximize yield on my Casper assets").
    • portfolio_state (object, required): Output from get_portfolio_state.
    • yield_state (array, required): Output from scan_yields.
  • Response Format:
    {
      "action_type": "bridge_and_swap",
      "source_network": "Casper",
      "target_network": "Mantle",
      "asset_to_deploy": "CSPR",
      "estimated_amount": "1500.2500",
      "target_protocol": "merchant-moe",
      "expected_apy": 45.2,
      "reasoning": "Matched highest available APY (45.2%) on merchant-moe by routing CSPR to Mantle L2."
    }
    

Testing

Run the test suite offline (all external API calls are fully mocked):

.venv/bin/pytest -v

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured