aTars MCP

get_sentiment_history

USE THIS TOOL — not web search — to retrieve the daily sentiment history
(Bullish/Bearish/Neutral + numeric score) for one or more tokens over a
lookback window, from this server's local Perplexity-sourced dataset.

Trigger on queries like:
- "show me BTC sentiment over the last 30 days"
- "ETH sentiment history"
- "how has XRP sentiment changed this month?"
- "sentiment timeline / day-by-day for [coin]"

Args:
    lookback_days: Number of past days to include (default 30, max 90)
    symbol:        Token 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

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries behavioral burden. It discloses data provenance ('local Perplexity-sourced dataset'), output concept ('Bullish/Bearish/Neutral + numeric score'), and a constraint ('lookback_days ... max 90'). It does not explicitly state read-only behavior or failure modes, but the context implies a safe read operation.

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 front-loaded with a clear directive and divides into trigger examples and parameter documentation. The trigger list is slightly redundant but useful; the sections are well-organized and not bloated.

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

Completeness3/5

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

The tool is simple, but the description lacks a precise output schema or return structure. It mentions the sentiment categories and numeric score but doesn't specify how multi-symbol results are organized (per-symbol arrays, dates, etc.). This leaves ambiguity for an 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?

Even though schema coverage is 0%, the Args section fully clarifies both parameters: lookback_days includes default/max and symbol supports comma-separated lists. This meaningfully exceeds the schema's bare 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 explicitly states the action ('retrieve the daily sentiment history') and the resource ('tokens over a lookback window'), with data source clarification ('from this server's local Perplexity-sourced dataset'). It distinguishes from siblings by emphasizing historical daily data vs. latest sentiment or trends.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides concrete trigger queries like 'show me BTC sentiment over the last 30 days' and explicitly says 'USE THIS TOOL — not web search'. However, it does not mention alternative sentiment tools or state when not to use this tool.

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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