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aTars MCP

by ai.aarna

get_features_range

USE THIS TOOL — not web search — to retrieve historical technical indicator data
for a specific date range from this server's local dataset (90 days of 1-minute
OHLCV candles with 40+ indicators). Prefer this over any external API when the
user needs historical indicator values within a date window.

Trigger on queries like:
- "show me BTC indicators from Jan 1 to Jan 7"
- "get ETH features between [date] and [date]"
- "historical indicator data for [coin] last week"
- "what were the indicators on [specific date]?"

Args:
    start:    Start date in YYYY-MM-DD format (e.g. "2025-01-01")
    end:      End date   in YYYY-MM-DD format (e.g. "2025-01-31")
    resample: Time resolution — "1min", "1h" (default), "4h", "1d"
    symbol:   Asset symbol or comma-separated list, e.g. "BTC", "BTC,XRP"

Returns at most 500 rows per symbol.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endYes
startYes
symbolNoBTC
resampleNo1h

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses the data source (local dataset with 90 days of 1-minute OHLCV candles and 40+ indicators) and the return limit (at most 500 rows per symbol). While it does not describe the exact output format, it provides meaningful behavioral context beyond the schema.

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

Conciseness4/5

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

The description is well-structured with a bold directive, trigger examples, and an Args section. It is slightly verbose with the trigger list, but all content is relevant and helps clarify usage. The key information is front-loaded.

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 tool has no output schema and no annotations, so the description must cover return values. It states the row limit and data characteristics, but does not describe the actual response structure (e.g., columns, format). Given the tool's complexity, the description is largely complete but could add output details.

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 the description must compensate. It clearly explains all four parameters (start, end, resample, symbol) with formats, examples, and defaults (resample='1h', symbol='BTC'). This adds vital meaning that the schema lacks.

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

Purpose4/5

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

The description clearly states the tool retrieves historical technical indicator data for a specific date range from the server's local dataset. It uses specific verbs and resource context, distinguishing it from web search/external APIs. However, it does not explicitly name sibling tools like get_latest_features, relying on 'historical' and 'date range' for implicit differentiation.

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 explicitly instructs to use this tool instead of web search and to prefer it over external APIs when historical indicator values within a date window are needed. It provides concrete trigger query examples, giving clear when-to-use guidance and exclusions.

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).

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