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GlynnCarson

tradingview-mcp

by GlynnCarson

market_sentiment

Read-only

Analyze news sentiment for stocks and crypto assets using Marketaux entity sentiment data. Specify a symbol, category, and article limit.

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 indicate readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds the licensed data source (Marketaux) as behavioral context, but does not discuss rate limits, pagination, or response structure. This adds modest value beyond annotations.

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 extremely concise: one-line purpose followed by a clean Args list. Every sentence is informative and there is no redundant text. It is front-loaded and easy to parse.

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 has no output schema and moderate complexity (3 parameters). The description explains parameters well but does not mention what the tool returns (e.g., a sentiment score, article list, or aggregated metric). This omission reduces completeness for agent planning.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage for parameters, so the description must compensate. It does so by providing examples for symbol, enumerating category values ('crypto', 'stocks', 'all'), and explaining limit as 'Max articles to analyse'. This covers all parameters meaningfully, though it could be more precise (e.g., symbol format).

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 states 'News sentiment for stocks and crypto', which clearly identifies the tool's function as retrieving sentiment analysis of news for given assets. However, it does not explicitly differentiate from the sibling tool 'financial_news' (which may fetch raw news) nor describe the output format, so clarity is slightly reduced.

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?

No guidance is provided on when to use this tool versus the many siblings (e.g., financial_news, market_snapshot). The description lacks any 'when to use' or 'when not to use' information, leaving the agent to infer context from the name alone.

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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