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Size-aware exit cost, depeg risk & liquidity fragility for a Base token (x402, testnet).
Size-aware exit cost, depeg risk & liquidity fragility for a Base token (x402, testnet).
Remote endpoints: streamable-http: https://agentdata-liquidity-exit-cost.onrender.com/mcp/
Valid MCP server (1 strong, 2 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
Endpoint verified · Open access · No issues found
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Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-phenicea-agentdata-liquidity-exit-cost": {
"url": "https://agentdata-liquidity-exit-cost.onrender.com/mcp/"
}
}
}From the project's GitHub README.
A pay-per-call data endpoint for AI agents: size-aware exit cost, depeg risk, and liquidity fragility for a token on Base. Not raw prices — computed, normalized, decision-grade intelligence an agent needs before it acts. Settled in USDC via x402 (Phase 2). Sourced on-chain, so the derived data is cleanly redistributable.
Strategy, values, and roadmap live in
CLAUDE.md. Decisions are logged indecisions/DECISION_LOG.mdanddecisions/adr/.
"If I try to exit this position right now, what does it actually cost me, how much can I move before slippage blows past X bps, and how fragile is that liquidity?"
Three priced tiers (single source of truth in agentdata/api/pricing.py):
| Tier | Contents | Mainnet price |
|---|---|---|
| quote | best-route exit cost for one size | $0.008 |
| risk | exit cost + fragility (+ depeg for pegged assets) — default | $0.02 |
| deep | risk + multi-size exit-cost curve + max-size-before-cost ladder | $0.04 |
Testnet forces every price to $0 (the 402 flow is still exercised end-to-end).
src/agentdata/
config.py # env-driven; NETWORK_MODE testnet|mainnet (testnet default)
compute/ # the value-add: pure, deterministic, unit-tested math
amm.py # constant-product + Solidly stable curve, exit cost
routing.py # cheapest-venue selection
depeg.py # depeg deviation / dispersion / score
fragility.py # depth + concentration + convexity -> fragility score
tiers.py # quote / risk / deep orchestration
chain/ # on-chain Base reads (Aerodrome / Uniswap), web3 lazy
provider.py # FixturePoolProvider (default) + factory
onchain.py # OnChainPoolProvider (env-gated, no guessed addresses)
api/ # FastAPI JSON layer + pricing + stable schema
monitoring/ # uptime, latency p50/p95, error rate, calls per tier
tests/ # unittest (stdlib for compute; fastapi for api)
pip install -e . # or: pip install fastapi pydantic 'uvicorn[standard]'
uvicorn agentdata.api.app:app --reload
# then:
curl 'http://127.0.0.1:8000/v1/liquidity/exit-cost?token=WETH&size=10&tier=risk'
curl 'http://127.0.0.1:8000/pricing'
curl 'http://127.0.0.1:8000/metrics'
Defaults: NETWORK_MODE=testnet, POOL_SOURCE=fixture (deterministic demo pools
WETH, THIN, USDX). See .env.example.
# compute core needs no dependencies:
PYTHONPATH=src python -m unittest discover -s tests
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