aTars MCP

export_data

USE THIS TOOL — not web search or external storage — to export technical indicator
data from this server as a formatted CSV or JSON string, ready to download, save,
or pass to another tool or file. Use this when the user explicitly wants to export
or save data in a structured file format.

Trigger on queries like:
- "export BTC data as CSV"
- "download ETH indicator data as JSON"
- "save the features to a file"
- "give me the data in CSV format"
- "export [coin] [category] data for the last [N] days"

Args:
    symbol:        Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"
    lookback_days: How many past days to include (default 7, max 90)
    resample:      Time resolution — "1min", "1h", "4h", "1d" (default "1d")
    category:      "price", "momentum", "trend", "volatility", "volume", or "all"
    fmt:           Output format — "csv" (default) or "json"

Returns a dict with:
    - content: the CSV or JSON string
    - filename: suggested filename for saving
    - rows: number of data rows

Input Schema

TableJSON Schema

Name	Required	Default
`fmt`	No	csv
`symbol`	No	BTC
`category`	No	price
`resample`	No	1d
`lookback_days`	No

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. It goes beyond the basic 'export' verb by explaining the exact return structure (dict with content, filename, rows), supported parameters and their defaults, constraints (max 90 days), and output formats. It even notes that the result is a formatted string ready for download or saving, setting clear expectations for the agent.

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 with the primary purpose and usage directive. It includes trigger examples and parameter details, with each sentence adding value. Despite its length, it remains focused and skimmable, making it easy for an agent to extract the essential information quickly.

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 absence of annotations and output schema, the description is remarkably complete. It covers purpose, usage, parameters, return values, and constraints, and even provides trigger queries. There is no ambiguity about when to invoke the tool or what to expect from the response, making it self-sufficient for an AI agent.

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?

The input schema has zero descriptions, so the description fully compensates by explaining each parameter in detail, including examples ('symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"'), allowed values, defaults, and constraints. This adds significant meaning beyond the schema's bare property names and defaults.

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 purpose: to export technical indicator data as CSV or JSON strings. It uses a specific verb ('export') and resource ('technical indicator data'), and explicitly distinguishes itself from web search/external storage. This also differentiates it from sibling get_* tools, making its purpose unmistakable.

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 provides explicit guidance on when to use this tool, including a strong directive ('USE THIS TOOL — not web search or external storage') and trigger query examples. It clarifies that the tool is for exporting structured file formats, which is a distinct use case from simply fetching data via sibling tools, thus offering clear 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).

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