> For the complete documentation index, see [llms.txt](https://docs.dapplooker.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.dapplooker.com/data-apis-for-ai.md).

# Data APIs for AI

- [Conclusion](https://docs.dapplooker.com/data-apis-for-ai/conclusion.md)
- [Contact Us](https://docs.dapplooker.com/data-apis-for-ai/contact-us.md)
- [x402 Payments](https://docs.dapplooker.com/data-apis-for-ai/x402-payments.md): Wallet-Based API Access for Agents and Autonomous Systems
- [Historical Market Data](https://docs.dapplooker.com/data-apis-for-ai/historical-market-data.md): Fetch historical daily metrics for any token, including USD price, trading volume, and market capitalisation. Ideal for backtesting, trend analysis, and building time-series visualisations.
- [Agent Intelligence](https://docs.dapplooker.com/data-apis-for-ai/agent-intelligence.md): Retrieve detailed metrics for specified AI agents, including market behaviour, technical indicators, and "smart money" movement patterns. Designed for deep analysis of agent strategies and
- [Token Directory](https://docs.dapplooker.com/data-apis-for-ai/token-directory.md): Get a complete list of indexed crypto tokens along with essential metadata. Useful for discovery, enumeration, and validation of active tokens across supported chains and protocols.
- [MCP / NLP driven Market Intelligence](https://docs.dapplooker.com/data-apis-for-ai/mcp-nlp-driven-market-intelligence.md): Run complex queries and extract chart-level insights using natural language. Powered by NLP and MCP-based APIs for agent-ready integration.
- [Staking Intelligence](https://docs.dapplooker.com/data-apis-for-ai/staking-intelligence.md): Provides real-time staking/unstaking data for tokens, including TVL, net staked, retention rates, and transaction-level insights - helping track participation, liquidity shifts, and on-chain sentiment
- [Trending Tokens](https://docs.dapplooker.com/data-apis-for-ai/trending-tokens.md): Access a list of tokens gaining real-time momentum based on aggregated market signals, user activity, and on-chain metrics. Ideal for surfacing high-velocity assets across tracked chains.
- [Hyperliquid Perp Trade Token Data](https://docs.dapplooker.com/data-apis-for-ai/hyperliquid-perp-trade-token-data.md): Provides real-time perpetual contract data for tokens on the Hyperliquid exchange. It returns live market prices, liquidity metrics, funding rates, and technical indicators to assist trading analysis.
- [Smart Money Trends](https://docs.dapplooker.com/data-apis-for-ai/smart-money-trends.md): Tracks net inflows/outflows by smart money wallets across 24h, 7d, and 30d periods. Helps identify accumulation or distribution patterns and spot tokens gaining attention from top market participants.
- [Unified Token Intelligence API](https://docs.dapplooker.com/data-apis-for-ai/unified-token-intelligence-api.md): Access consolidated token data through a single endpoint - combining market metrics, technical indicators, on-chain activity, and social sentiment.
- [ACP Activity Engine (Coming Soon)](https://docs.dapplooker.com/data-apis-for-ai/acp-activity-engine-coming-soon.md)
- [Live Hyperliquid Perp Strategy](https://docs.dapplooker.com/data-apis-for-ai/live-hyperliquid-perp-strategy.md): Provides real-time, AI-powerd perp trading strategies for Hyperliquid markets. gives actionable strategies with clear entry zones, risk parameters, leverage guidance, and execution-ready action object
- [GET: Agent Metadata](https://docs.dapplooker.com/data-apis-for-ai/get-agent-metadata.md)


---

# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.dapplooker.com/data-apis-for-ai.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.