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omniologynow-rgb/profitspot-mcp

by Various

The DeFi Brain for AI agents — give Claude the power to grade 6,500 liquidity pools across 86 chains. Risk scoring, Monte Carlo sims, impermanent loss, whale tracking. pip install

O

MCP

omniologynow-rgb/profitspot-mcp

Added 1 June 2026

#ai-agent #blockchain #claude #copilot #cross-chain #cursor #decentralized-finance #defi

Overview

A Python package that lets AI agents analyze over 6,500 liquidity pools across 86 blockchains. It provides risk scoring, Monte Carlo simulations, impermanent loss calculations, and whale tracking through a Model Context Protocol interface.

Best for

Best for
Developers building AI agents that need on-chain DeFi risk analysis across multiple blockchains

Use cases

  • Evaluating liquidity pool risk before providing liquidity
  • Simulating potential returns and impermanent loss scenarios
  • Monitoring whale movements across multiple chains

Notes

A Python package that lets AI agents analyze over 6,500 liquidity pools across 86 blockchains. It provides risk scoring, Monte Carlo simulations, impermanent loss calculations, and whale tracking through a Model Context Protocol interface.

0 stars on GitHub. Last updated 2026-05-03. Licensed AGPL-3.0.

Use cases

  • Evaluating liquidity pool risk before providing liquidity
  • Simulating potential returns and impermanent loss scenarios
  • Monitoring whale movements across multiple chains

Pros

  • Covers a wide range of chains and pools for broad DeFi analysis
  • Includes advanced risk tools like Monte Carlo simulations
  • Easy to install and integrate with AI agents via pip

Cons

  • Zero stars on GitHub suggests limited community adoption or testing
  • Requires understanding of DeFi concepts to interpret results
  • No documented examples or usage guides beyond the install command

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Covers a wide range of chains and pools for broad DeFi analysis
  • Includes advanced risk tools like Monte Carlo simulations
  • Easy to install and integrate with AI agents via pip

Cons

  • Zero stars on GitHub suggests limited community adoption or testing
  • Requires understanding of DeFi concepts to interpret results
  • No documented examples or usage guides beyond the install command