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rkilchmn

tradingview-mcp-server

by rkilchmn

market_sentiment

Read-only

Analyze market sentiment for stocks and crypto by fetching and evaluating news articles for a given symbol. Get a sentiment score to gauge bullish or bearish market mood.

Instructions

News sentiment for stocks and crypto (licensed Marketaux entity sentiment).

Args: symbol: Asset symbol ("AAPL", "BTC", "ETH", "TSLA") category: News group to search ("crypto", "stocks", "all") limit: Max articles to analyse

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
symbolYes
categoryNoall
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, establishing it as a safe read-only operation. The description adds a small behavioral note about limit controlling the number of articles analyzed and mentions the licensed data source, but it does not describe the output format or any rate limits. This is moderate transparency.

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 a single-line purpose plus a compact Args block. It contains no filler and is front-loaded with the essential behavior, every sentence earning its place.

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

Completeness2/5

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

No output schema is provided, and the description does not explain what the result looks like (score, label, articles). Since the tool's purpose is sentiment analysis, agents need to know whether to expect a numeric score, categorical rating, or article list. The licensed source mention adds some context, but the missing return format is a significant gap.

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 provides only types and defaults (0% description coverage), so the description fully compensates by documenting symbol with examples, category with enumerated values, and limit as max articles. This is exemplary parameter documentation for a low-coverage schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides news sentiment for stocks and crypto, and names the data source (Marketaux). This makes its core function evident and distinguishes it from sibling tools like financial_news or top_gainers, though it lacks an explicit verb and does not name a specific sibling.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives. It does not mention that financial_news might be more appropriate for raw articles, nor does it indicate when sentiment analysis is the right choice. The agent must infer usage solely from the tool name and brief description.

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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