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

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

AddedJul 31, 2026

TDQS

A5/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it excels. It discloses the computation window (last 260 closed candles), how insufficient history is handled ('Indicators without enough closed-candle history render as n/a'), that candle count is always reported, and that VWAP is n/a on Hyperliquid 5m-1h due to NULL volume. It also previews the output structure (snapshot header plus indicator rows with trend deltas). No annotation contradictions exist.

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?

The description is long but information-dense, with clear section separation (Supports, You-must-use, Timeframes, Output, n/a notes, Examples). Every sentence contributes meaningful operational detail; there is no fluff or repetition. The front-loaded purpose sentence immediately orients the agent, and the structured format makes the extended content easy to parse.

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?

Given the tool's complexity (7 indicator families, 7 timeframes, 3 chain types, perp naming variants, edge cases), the description is remarkably complete. It covers input semantics, output shape, limitations, and usage rationale. An output schema exists, so return values don't need schema-level explanation, but the description still provides a high-level output preview. This is a model example of contextual completeness for a data-analysis tool.

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 description coverage is 0%, so the description must compensate for the single 'request' wrapper with no inline documentation. It does so comprehensively: it explains every nested parameter (chain, tokenAddress, timeframe), gives valid values (timeframe enum, chain examples), shows Hyperliquid perp symbol conventions ('BTC', 'HYPE', 'XYZ:ORDI'), and includes two concrete JSON examples. This fully covers the semantics that the schema lacks.

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?

The description opens with a specific verb+resource+scope: 'Get a technical-analysis snapshot for a token' followed by an exhaustive list of indicators (SMA, EMA, RSI, MACD, Bollinger, ATR, VWAP) and the data window (260 closed candles). It clearly distinguishes itself from siblings like token_ohlcv by explicitly stating it computes indicators rather than returning raw candles, and it explains its multi-chain support (EVM, Solana, Hyperliquid). This is far beyond a vague or tautological statement.

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?

The description gives explicit when-to-use guidance: 'YOU MUST USE THIS for technical analysis instead of computing indicators from raw token_ohlcv candles' with rationale (more history, charting-platform conventions). It also provides timeframe selection guidance (5m/15m/30m/1h for intraday, 1d for swing, 1w for long-term) and warns when VWAP is unavailable (Hyperliquid 5m-1h). It names the alternative tool (token_ohlcv) and explains why this tool is preferred, fully satisfying the usage-guidelines criterion.

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

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TDQS

A3.6/5.0

Disambiguation4/5

Most tools have clearly distinct purposes (address vs token vs prediction market vs transaction). Overlapping tools like token_discovery_screener and nansen_score_top_tokens, or address_counterparties and address_related_addresses, are explicitly disambiguated with detailed usage guidance. A few tools (token_flows vs token_recent_flows_summary) require careful reading but are still distinguishable.

Naming Consistency4/5

Tool names consistently use snake_case with resource prefixes (address_*, token_*, prediction_market_*). Minor deviations include general_search, growth_chain_rank, and nansen_score_top_tokens, and some names like smart_traders_and_funds_perp_trades are verbose but still follow the pattern.

Tool Count3/5

38 tools is a heavy surface, but it matches Nansen's broad domain covering address analytics, token intelligence, prediction markets, and Hyperliquid perps. The count is on the high end and may overwhelm agents, but each tool appears to serve a distinct function within a reasonably scoped analytics platform.

Completeness5/5

The tool surface is comprehensive: address lifecycle (portfolio, transactions, balances, counterparties, PnL), token analytics (price, OHLCV, indicators, flows, transfers, holders, discovery), prediction markets (lookup, orderbook, trades, top holders, PnL), and specialized features (smart money, chain rankings). No obvious dead ends or missing critical operations are apparent.

Resources

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