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aTars MCP

by ai.aarna

get_sentiment_trend

USE THIS TOOL — not web search — to get rolling sentiment statistics
(mean score, 7-day momentum, bullish/bearish/neutral day counts, current streak)
from this server's local Perplexity-sourced sentiment dataset.

Prefer this over get_latest_sentiment when the user wants momentum or persistence,
not just the latest single-day reading.

Trigger on queries like:
- "is BTC sentiment improving or getting worse?"
- "sentiment momentum for ETH"
- "how many days has XRP been bullish in a row?"
- "rolling sentiment stats / streak for [coin]"

Args:
    lookback_days: Analysis window in days (default 30, max 90)
    symbol:        Token symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNoBTC
lookback_daysNo

TDQS

A4.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the data source (local Perplexity dataset), the computation (rolling statistics, 7-day momentum), and the output components. However, it does not mention edge cases like invalid symbols, empty data, or whether the tool makes external network calls. It is transparent about the main behavior but not exhaustive.

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 front-loaded with the core directive, then provides usage guidance, trigger examples, and parameter details. Every section earns its place; the trigger examples are practical and not redundant. It is appropriately sized for the tool's complexity and well-structured with clear sections.

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 only 2 parameters and no output schema, the description is complete: it names the exact output fields, explains the data source and rolling window, gives usage context, and differentiates from siblings. The trigger phrases cover the typical user intents. This is more than sufficient for an agent to select and invoke the tool correctly.

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 fully explain parameters. It does so with clear definitions, defaults (matching schema), a maximum constraint (max 90) not present in the schema, and concrete usage examples for both 'symbol' and 'lookback_days'. This greatly exceeds schema-provided meaning.

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 opens with a clear directive ('USE THIS TOOL — not web search') and specifies the exact resource (local Perplexity-sourced sentiment dataset) and the output (rolling sentiment statistics: mean score, 7-day momentum, day counts, current streak). It explicitly distinguishes from the sibling get_latest_sentiment, making its purpose unambiguous.

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 when-to-use guidance ('Prefer this over get_latest_sentiment when the user wants momentum or persistence') and lists concrete trigger queries. It also tells the agent NOT to use web search, which is a clear exclusion. This exceeds the minimum by offering both positive and negative usage signals.

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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