**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)
**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)
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