Mario A2MCP Intelligence Suite
Multi-endpoint Web3 intelligence service for token risk scanning, pre-trade checks, and signal snapshots, using DexScreener data with x402 payment support.
README
Mario A2MCP Intelligence Suite
Multi-endpoint A2MCP-style Web3 intelligence service for OKX.AI listing experiments.
The current suite exposes three separately listable services on one shared runtime:
| Service | Endpoint | Positioning |
|---|---|---|
| Token Risk Guard | POST /api/token-risk-scan |
Professional token risk scan for liquidity, volatility, data availability, and review hints. |
| ApeGuard | POST /api/ape-pretrade-check |
Short pre-trade meme/new-token check with an ape_score and decision hint. |
| Web3 Signal Snapshot | POST /api/signal-snapshot |
Generic Web3 signal snapshot for tokens, wallets, projects, or risk objects. |
The first production direction is Token Risk Guard plus ApeGuard. They share the same data, scoring, and x402 payment layer, so more A2MCP endpoints can be added without rebuilding payment and deployment from scratch.
Current Data Sources
- DexScreener token pairs API for public DEX liquidity, price, volume, pair age, and transaction-count fields.
- Holder concentration and contract-permission fields are explicitly returned as unavailable in this MVP. The service does not infer or fabricate them.
Run Locally
npm start
Then call:
curl -s http://localhost:8787/health
curl -s http://localhost:8787/metadata
curl -s http://localhost:8787/mcp
curl -s http://localhost:8787/openapi.json
Token Risk Guard:
curl -s -X POST http://localhost:8787/api/token-risk-scan \
-H 'content-type: application/json' \
-d '{
"chain": "solana",
"token_address": "So11111111111111111111111111111111111111112",
"language": "zh-CN"
}'
ApeGuard:
curl -s -X POST http://localhost:8787/api/ape-pretrade-check \
-H 'content-type: application/json' \
-d '{
"chain": "solana",
"token_address": "So11111111111111111111111111111111111111112",
"mode": "quick",
"language": "zh-CN"
}'
Signal Snapshot:
curl -s -X POST http://localhost:8787/api/signal-snapshot \
-H 'content-type: application/json' \
-d '{
"chain": "solana",
"address": "So11111111111111111111111111111111111111112",
"mode": "token",
"question": "给我一个代币风险和市场信号快照",
"lookbackHours": 24,
"language": "zh-CN"
}'
MCP-style tool call:
curl -s -X POST http://localhost:8787/mcp \
-H 'content-type: application/json' \
-d '{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "token_risk_scan",
"arguments": {
"chain": "solana",
"token_address": "So11111111111111111111111111111111111111112",
"language": "zh-CN"
}
}
}'
One-command Tests
The smoke tests start and stop a local server automatically.
npm test
npm run test:x402
Run Mock x402 Mode
This mode does not settle real payments. It verifies that unpaid requests return HTTP 402 plus a PAYMENT-REQUIRED header.
PORT=8788 \
PAYMENT_MODE=mock-x402 \
PUBLIC_BASE_URL=https://your-public-domain.example \
npm start
Then test any endpoint:
onchainos agent x402-check \
--endpoint http://localhost:8788/api/token-risk-scan \
--body '{"chain":"solana","token_address":"So11111111111111111111111111111111111111112"}'
onchainos agent x402-check \
--endpoint http://localhost:8788/mcp \
--body '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'
Production x402 Mode
Production payment mode requires OKX facilitator credentials and a real receiving address. Do not enable this until the receiving wallet and OKX payment setup are confirmed.
PORT=8788 \
PAYMENT_MODE=okx-x402 \
OKX_API_KEY=... \
OKX_SECRET_KEY=... \
OKX_PASSPHRASE=... \
X402_PAY_TO=0xYourReceivingAddress \
X402_PRICE='$0.02' \
X402_NETWORK=eip155:196 \
X402_ASSET=0x779ded0c9e1022225f8e0630b35a9b54be713736 \
X402_AMOUNT_MINIMAL=20000 \
X402_TOKEN_NAME='USDt0' \
X402_TOKEN_SYMBOL=USDT0 \
X402_TOKEN_DECIMALS=6 \
npm start
X402_PRICE is kept for listing/metadata display. The actual payment challenge uses the explicit asset amount fields so validators can read token decimals without guessing.
Deployment Shape
This folder includes deployment starters:
.env.example: local and production environment variables.Dockerfile: container build for Node 22.14.deploy/systemd/a2mcp-signal-snapshot.service: Linux service template.deploy/nginx/a2mcp-signal-snapshot.conf: reverse proxy template before adding HTTPS.
For OKX.AI A2MCP listing, the endpoint should be a stable public HTTPS URL. Localhost and temporary tunnels are only useful for development checks.
Set PUBLIC_BASE_URL to the HTTPS origin so /metadata can include complete endpointUrl fields for each listable service.
Candidate OKX.AI A2MCP Listings
Token Risk Guard:
面向交易 Agent、研究 Agent 和钱包风控场景,按次返回结构化 Token 风险扫描结果,包括 risk_score、risk_level、flags、liquidity、holders、contract、suggested_action、data_quality 和 source URLs。第一版接入公开 DEX 数据源;未覆盖的数据会明确标记 unavailable,不编造。
Endpoint:
https://<your-domain>/api/token-risk-scan
MCP endpoint:
https://<your-domain>/mcp
ApeGuard:
面向 meme / 新币交易前体检场景,输入 chain 和 token_address,返回 ape_score、risk_level、one_line、red_flags、market_status 和 decision_hint。输出只做风险提示,不构成买卖建议,也不执行交易。
Endpoint:
https://<your-domain>/api/ape-pretrade-check
Suggested fee:
0.02 USDT / call
Production Checklist
- Deploy to a stable public HTTPS endpoint.
- Add more verifiable read-only data sources: explorer/security API for contract checks, holder distribution, and optional GoPlus/Honeypot providers.
- Keep missing-data fields explicit with
data_quality,available,reason, and source status. - Switch from
mock-x402tookx-x402with real OKX facilitator credentials and receiving address. - Add request logging without storing user secrets.
- Add rate limiting and timeout controls.
- Test with a real agent client before registering on OKX.AI.
Recommended Servers
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.
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.
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.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
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.
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.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
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.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.