Skip to main content
Glama

aTars MCP

by ai.aarna

get_sentiment_signal

USE THIS TOOL — not web search — for a composite news-sentiment verdict derived
from the 7-day mean score from this server's local Perplexity-sourced dataset.

Emits: STRONG BULLISH, BULLISH, NEUTRAL, BEARISH, or STRONG BEARISH.

Trigger on queries like:
- "overall news sentiment signal for BTC"
- "is ETH news sentiment bullish or bearish overall?"
- "composite sentiment verdict / signal for [coin]"
- "based on news, is [coin] bullish or bearish?"

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNoBTC

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the data source (local Perplexity-sourced dataset) and computation (7-day mean), which adds useful context. However, it does not clarify behavior for comma-separated symbols (single vs. multiple outputs) or handle missing data, leaving ambiguity.

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 compact and front-loaded with a directive, followed by output values, trigger examples, and argument documentation. Each section serves a distinct purpose with no redundant text.

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?

While the description lists possible output values, it does not specify the exact return structure for multiple symbols or error conditions. Given the absence of an output schema, this leaves a gap in understanding what a successful call returns. However, it does cover the key trigger context and argument format.

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 schema has only one parameter 'symbol' with no description, but the tool description fully explains it: 'Token symbol or comma-separated list' with examples. This is rich semantic detail beyond the schema, compensating for the 0% schema coverage.

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 identifies the tool as returning a composite news-sentiment verdict derived from a 7-day mean from a local dataset. It lists the exact output values and explicitly directs users away from web search, distinguishing its purpose. The specificity of 'composite' and '7-day mean' differentiates it from sibling sentiment tools.

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?

The description explicitly instructs to use this tool instead of web search for composite sentiment verdicts, and provides four example query phrasings. It does not explicitly name sibling tools as alternatives, but the trigger examples give clear context for when to invoke it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All aTars MCP toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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