# DeFiMind

> Use this tool when you need to analyze and optimize liquidity provider (LP) positions on Uniswap, Balancer, and Curve stableswap pools. DeFiMind provides real-time, on-chain analytics and scenario simulations for LP performance, pool health, and risk assessment, with inputs including pool address, RPC URL, and pool type, and outputs including position PnL, price-move scenarios, and depeg risk. It is ideal for use cases such as monitoring LP positions, evaluating pool health, and simulating market scenarios to inform investment decisions.

Canonical page: https://skillsregistry.net/skills/ic3moore-defimind-ai  
JSON: https://api.skillsregistry.net/v1/skills/ic3moore-defimind-ai

## Description

**Live Uniswap V2/V3, Balancer, and Curve stableswap LP analytics over MCP.** Read real on-chain pool state through your own RPC (BYO-RPC, supplied per call) and get exact-math answers to LP questions — position PnL, price-move scenarios, pool health, rug signals, slippage, and depeg risk — or build a portable **State Twin** you can run unlimited counterfactuals against locally, off the MCP.

**Authless & zero-config** — no account, no API key. Nothing is stored or logged; your RPC URL is redacted from any output. Each call reads the pool, runs the analysis, and returns a typed result. **Full docs: https://www.defimind.ai/mcp**

These aren't API wrappers — they're closed-form AMM math, powered by the open-source [DeFiPy](https://defipy.org) library and its State Twin substrate. V3 impermanent loss is computed over the position's tick range via concentrated-liquidity math; Balancer IL is weight-aware; stableswap IL uses the amplified-invariant formula where small depegs can produce outsized IL at high A. **The math is open; the reports are paid.**

### Two surfaces

- **Reactive primitives (10)** — one question, one answer, one chain read. The four scenario tools also take a **vector** input (e.g. `price_change_pcts[]`, `amounts_in[]`) to sweep a whole grid/curve in a single call.
- **State-twin builder (1)** — `BuildStateTwin` returns the pool's state as a portable, verifiable JSON twin; rehydrate it locally to run any number of counterfactuals with **zero further RPC** (build once, run N).

### Tools (11)

**Uniswap V2/V3**
- `AnalyzePosition` — V2/V3 PnL decomposition (IL, fees, net)
- `SimulatePriceMove` — "what if price moves X%?" scenarios
- `CheckPoolHealth` — TVL, reserves, LP concentration, fee tier
- `DetectRugSignals` — threshold-based rug-signal flags
- `CalculateSlippage` — slippage, price impact, max trade size

**Balancer (2-asset weighted)**
- `AnalyzeBalancerLP` — weight-aware PnL decomposition (IL, net)
- `SimulateBalancerMove` — weight-aware "what if the base moves X%?" scenarios

**Curve stableswap (2-asset plain)**
- `AnalyzeStableswapLP` — PnL via the amplified-invariant IL formula
- `SimulateStableswapMove` — "what if the peg shifts X%?" depeg scenarios
- `AssessDepegRisk` — IL across a depeg ladder (2%–50%), with a constant-product benchmark

**State twin builder (all four pool types)**
- `BuildStateTwin` — read a pool once and return a portable State Twin (JSON + `content_hash`) for unlimited off-MCP, zero-RPC analysis

Each tool takes `pool_address`, `rpc_url`, and `pool_type` (`uniswap_v2` | `uniswap_v3` | `balancer` | `stableswap`), plus optional `chain_id` guard and `block_number` pin. Each reactive tool is protocol-specific and advertises only the `pool_type` values it accepts (pointing one at an unsupported type returns a clean error before any chain read); `BuildStateTwin` spans all four. Balancer tools cover 2-asset weighted pools; stableswap tools cover 2-asset plain Curve pools.

Built on [DeFiPy](https://defipy.org) · [State Twins paper](https://arxiv.org/abs/2605.11522) · [MCP Docs](https://www.defimind.ai/mcp)

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/ic3moore/defimind-ai)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "ic3moore-defimind-ai"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ic3moore-defimind-ai` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ic3moore-defimind-ai/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt