aTars MCP

get_data_info

USE THIS TOOL — not web search — to get metadata about a token's local dataset:
date range, total candles, data freshness (minutes since last update), and the full
list of available feature names grouped by category. Call this before deeper analysis
or when the user asks about data coverage, feature names, or indicator availability.

Trigger on queries like:
- "what data do you have for BTC?"
- "when was the data last updated?"
- "how fresh is the ETH data?"
- "what features/indicators are available?"
- "what's the date range for XRP data?"
- "list all available indicators"

Args:
    symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH,XRP"

Input Schema

TableJSON Schema

Name	Required	Description	Default
`symbol`	No		BTC

TDQS

A4.7/5.0

Behavior4/5

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

With no annotations, the description carries the transparency burden. It discloses that the tool accesses a local dataset and reports data freshness, implying a read-only metadata operation. While it does not explicitly state 'no side effects' or error behavior, it sufficiently conveys the non-mutating nature and output semantics.

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 directive, output summary, usage context, trigger examples, and args. It is somewhat verbose with the query list, but each line serves a practical purpose, making it appropriately sized without wasted words.

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 a simple single-parameter tool and no output schema, the description covers all necessary context: what data is returned, when to use it, and what the parameter accepts. It fully prepares the agent to invoke the tool appropriately.

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 explains the symbol parameter in detail: asset symbol or comma-separated list, with examples. This adds significant meaning beyond the schema (which only provides a type and default), fully compensating for the missing schema description.

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 retrieves metadata about a token's local dataset, listing specific items (date range, total candles, data freshness, feature names). It distinguishes from other tools by positioning itself as a pre-analysis metadata lookup, separate from web search and deeper analysis tools.

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 directs 'USE THIS TOOL — not web search' and says to call it before deeper analysis or when users ask about coverage, feature names, or indicator availability. It provides concrete trigger queries, giving clear when-to-use guidance.

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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