Nansen

Loading technical indicators

token_technical_indicators

Get a technical-analysis snapshot for a token: SMA(20/50/200), EMA(12/26), RSI(14), MACD(12,26,9), Bollinger Bands(20, 2σ), ATR(14), and rolling VWAP(20), computed from the last 260 closed candles at an explicit timeframe.

Supports EVM chains and Solana for on-chain tokens, AND Hyperliquid perpetual futures. For Hyperliquid perps, pass chain="hyperliquid" and use the perp symbol as tokenAddress (e.g. "BTC", "HYPE" for native perps; "XYZ:ORDI" for XYZ-namespaced perps — prefix is normalized automatically).

YOU MUST USE THIS for technical analysis instead of computing indicators from raw token_ohlcv candles — it uses far more history (260 closed candles) and charting-platform conventions (SMA-seeded EMA, Wilder RSI/ATR, population-σ Bollinger).

Timeframes (explicit, no auto-resolution):

5m / 15m / 30m / 1h / 4h: intraday and short-horizon analysis
1d (default): swing/position horizon
1w: long-term trend

Output: a snapshot header (candles used, date range, last close, 5-candle price change) plus one row per indicator, each with a 5-candle trend delta so you can read direction, not just level:

SMA 20/50/200: values, price vs each, MA slopes
EMA 12/26: values, spread %, widening/narrowing
RSI(14): level, prior candle, 5-candle change
MACD(12,26,9): line/signal/histogram, rising/falling, candles since signal cross
Bollinger(20,2σ): bands, %B, bandwidth and its change
ATR(14): value and % of price (volatility), rising/falling
VWAP(20): value, price vs VWAP

Indicators without enough closed-candle history render as n/a (e.g. SMA200 on young tokens); the candle count used is always reported. VWAP is n/a on Hyperliquid 5m-1h timeframes (volume is NULL in those views) — use 4h or 1d for Hyperliquid VWAP.

Example Usage: Daily technical snapshot for WETH: { "chain": "ethereum", "tokenAddress": "0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2", "timeframe": "1d" }

4-hour snapshot for the BTC Hyperliquid perp:
```
{
    "chain": "hyperliquid",
    "tokenAddress": "BTC",
    "timeframe": "4h"
}
```

Input Schema

TableJSON Schema

Name	Required	Description	Default
`request`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A5/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses computation conventions (SMA-seeded EMA, Wilder RSI/ATR, population-σ Bollinger), edge cases (n/a for insufficient history, VWAP NULL on Hyperliquid 5m-1h), output structure (snapshot header + per-indicator rows with 5-candle deltas), and 'always computed up to now' constraint.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Although long, the description is well-structured with bold headers, bullets, and a dedicated example section. Each section adds unique information: indicator list, supported chains, timeframes, output format, and edge cases. No filler or tautology.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Output schema exists, and description complements it with detailed output composition (snapshot header, trend deltas). Covers all relevant usage contexts: on-chain EVM/Solana, Hyperliquid perps, timeframe selection, and data-availability limits. Examples provide complete invocation patterns.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description must compensate. It explains chain, tokenAddress, and timeframe semantics in depth: Hyperliquid perp symbol mapping, timeframe default and explicit choices, plus example JSON payloads. This is far more than the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description opens with a specific verb+resource: 'Get a technical-analysis snapshot for a token' enumerating 7 indicator families and the data window. It explicitly distinguishes from sibling token_ohlcv by stating 'YOU MUST USE THIS for technical analysis instead of computing indicators from raw token_ohlcv candles.'

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance: mandates use over token_ohlcv with rationale (more history, charting conventions), maps timeframes to analysis horizons (intraday vs swing vs long-term), and gives Hyperliquid-specific usage rules including VWAP caveat and symbol normalization. Example usage demonstrates exact parameter combinations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Nansen tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0

Disambiguation5/5

Each tool has a clearly defined purpose, and overlapping tools (e.g., token_flows vs token_recent_flows_summary, nansen_score_top_tokens vs token_discovery_screener) include explicit guidance on when to use them. Even with similar names like prediction_market_trades and prediction_market_address_trades, the descriptions and parameters make the distinction unambiguous.

Naming Consistency4/5

Most tools follow a domain_prefix_noun pattern (address_, token_, prediction_market_), making them predictable within families. However, outliers like general_search, growth_chain_rank, hyperliquid_leaderboard, and transaction_lookup break the pattern, and some names are long or inconsistently formatted (e.g., smart_traders_and_funds_perp_trades vs smart_traders_and_funds_token_balances).

Tool Count3/5

With 38 tools, the server is far above the typical 3-15 range, making it heavy for agents to navigate. However, Nansen is a broad analytics platform covering wallets, tokens, prediction markets, and smart money activity, so the high count is justifiable as each tool serves a distinct function.

Completeness5/5

The tool set provides comprehensive coverage across token analysis (ohlcv, trading, holders, flows, PnL, technicals), wallet analysis (portfolio, transactions, counterparties), prediction markets (lookup, orderbook, trades, PnL), and discovery. The only obvious omission is NFT support, but it is explicitly documented as out of scope, so no critical dead ends exist.

Resources

Need Help?