aTars MCP

get_signal_history

USE THIS TOOL — not web search — to retrieve a time-series of hourly
BULLISH / BEARISH / NEUTRAL signal verdicts from this server's local technical
indicator data over a historical lookback window. Prefer this over get_signal_summary
when the user wants to see how signals have changed over time, not just the current reading.

Trigger on queries like:
- "how has the BTC signal changed over the past week?"
- "show me ETH signal history"
- "was XRP bullish yesterday?"
- "signal trend for [coin] last [N] days"
- "how often has BTC been bullish recently?"

Args:
    lookback_days: Days of signal history (default 7, max 30)
    symbol:        Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

Input Schema

TableJSON Schema

Name	Required	Description	Default
`symbol`	No		BTC
`lookback_days`	No

TDQS

A4.4/5.0

Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses the data source (server's local technical indicator data), granularity (hourly), and verdict types, but does not mention response format, pagination, or any potential side effects. For a read-only retrieval, the absence of explicit 'no mutation' statement is a gap.

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 clear directive, usage context, trigger examples, and parameter details. The trigger query list is repetitive but valuable for an agent. Slightly longer than minimal, but every section earns its place.

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?

With no output schema, the description gives a good sense of what is returned (time-series of hourly signal verdicts) but does not specify the exact response structure. It covers parameters, defaults, and usage context, making it reasonably complete for a two-parameter read 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 has no per-parameter descriptions (0% coverage), but the description compensates fully with an 'Args' section explaining lookback_days (default 7, max 30) and symbol (single or comma-separated list), including examples. This adds meaning far beyond the 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 retrieves 'a time-series of hourly BULLISH / BEARISH / NEUTRAL signal verdicts' from local technical indicator data. It distinguishes itself from siblings by explicitly saying 'Prefer this over get_signal_summary' for historical changes.

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?

Provides explicit guidance: 'USE THIS TOOL — not web search' and 'Prefer this over get_signal_summary when the user wants to see how signals have changed over time'. Also includes concrete trigger query examples.

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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