aTars MCP

get_category_features

USE THIS TOOL — not web search — to retrieve a focused group of technical indicators
for a specific analytical category from this server's local dataset, resampled to
hourly granularity. Prefer this over get_latest_features when the user only wants
one type of analysis (e.g. only momentum or only volatility).

Categories and the indicators they include:
- "momentum": RSI, MACD, Stochastic, CCI, Williams %R, ROC, MOM
- "trend":    EMA_9/20/50, SMA_20, ADX, DMP/DMN, Ichimoku
- "volatility": Bollinger Bands (upper/lower/mid/width/pct), ATR, NATR
- "volume":   OBV, VWAP, MFI, volume_zscore, buy_sell_ratio
- "price":    OHLCV, returns_1/3/7, hl_spread, price_vs_ema20
- "all":      All of the above

Trigger on queries like:
- "show me BTC momentum indicators"
- "what are the trend indicators for ETH?"
- "volatility data for XRP this week"
- "volume analysis for SOL last 5 days"

Args:
    category:      One of the category names listed above
    lookback_days: Days of history (default 5, max 30)
    symbol:        Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

Input Schema

TableJSON Schema

Name	Required	Default
`symbol`	No	BTC
`category`	Yes
`lookback_days`	No

TDQS

A4.7/5.0

Behavior4/5

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

The description includes important behavioral details: it works on a local dataset, resamples to hourly granularity, supports comma-separated symbols, and has a max lookback of 30 days. Since no annotations are provided, the description carries the burden; it does not explicitly say 'read-only' but uses 'retrieve' and describes no mutating actions, which is adequate. However, it could be more explicit about potential side effects or restrictions.

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 well-structured and front-loaded, starting with a strong directive and then providing essential details in organized sections. Every sentence adds value: the category list, triggers, and parameter explanations are all necessary, with no fluff.

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

Completeness4/5

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

The description is quite complete given the tool's complexity: it defines all categories, lists indicators, and gives parameter constraints. However, without an output schema, it does not explicitly describe the return format or shape of the response, which would have made it fully self-contained.

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%, but the description fully compensates by explaining each parameter: category with its allowed values and what indicators they include, lookback_days with default and max, and symbol with format examples. This adds meaning far beyond 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?

The description clearly states the tool's function: it retrieves a focused group of technical indicators for a specific analytical category from the local dataset, resampled to hourly granularity. It explicitly distinguishes itself from get_latest_features, making its purpose unambiguous.

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 usage guidance, stating 'Prefer this over get_latest_features when the user only wants one type of analysis' and provides concrete trigger query examples. This tells the agent exactly when to use this tool versus alternatives.

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

A3.9/5.0

Disambiguation2/5

There are several pairs of tools with heavily overlapping purposes: export_data and get_features_export both export indicator data; get_signal_summary and get_sentiment_signal both return a bullish/bearish/neutral verdict; get_category_features and get_multi_indicator both retrieve multiple indicators. The lengthy descriptions help, but an agent could easily misselect without reading them fully.

Naming Consistency3/5

Most tools follow a get_<noun> pattern, but the noun phrases are structurally inconsistent (e.g., get_latest_features vs get_features_export vs get_features_range). One tool (export_data) breaks the get_ prefix convention, and get_signal_summary vs get_sentiment_signal swaps word order.

Tool Count3/5

With 18 tools, the server leans into the heavy range. Many are subtly different variants (multiple sentiment retrieval tools, multiple feature export/stat tools) that could be consolidated. Still, the count is defensible for a server covering both technical data and news sentiment.

Completeness4/5

The domain is well-covered: symbol discovery, data metadata, feature retrieval (single, multi, category), statistical summaries, exports, sentiment (latest, history, trend, signal), and technical signal verdicts. Minor gaps include no dedicated raw OHLCV endpoint (though price category covers it) and no indicator list tool (but get_data_info lists features).

Resources

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