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MaverickMCP

MaverickMCP

by wshobson
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

data_get_news_sentiment

Analyze news sentiment for a stock ticker, aggregating articles and scoring bullish or bearish outlook via Tiingo API or AI fallback.

Instructions

Get news sentiment analysis for a stock using Tiingo News API or LLM analysis.

This enhanced tool provides reliable sentiment analysis by:

  • Using Tiingo's news API if available (requires paid plan)

  • Analyzing sentiment with LLM (Claude/GPT)

  • Falling back to research-based sentiment

  • Never failing due to missing EXTERNAL_DATA_API_KEY

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ticker": "<ticker>"
    },
    "name": "data_get_news_sentiment"
  }
}

Input Schema

NameRequiredDescriptionDefault
tickerYesStock ticker symbol
timeframeNoTime frame for news (1d, 7d, 30d, etc.)7d
limitNoMaximum number of news articles to analyze

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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 behavioral traits: uses multiple sources (Tiingo, LLM, research), has fallback logic, and never fails due to missing API key. This is good transparency for a data retrieval tool.

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 detailed but not overly long, uses bullet points to list features, and is front-loaded with the main purpose. Some repetition could be trimmed, but overall it is appropriately sized.

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

Completeness4/5

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

Given no output schema and no annotations, the description does a good job explaining the tool's behavior, reliability, and inputs. It covers error handling (never failing due to missing API key). Could mention output format, but is fairly complete.

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

Parameters3/5

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

Schema coverage is 100% with all parameters described. The description adds value by explaining the tool's behavior with parameters (limit, timeframe) and fallback logic, but does not add significant meaning beyond the schema descriptions.

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 states the tool gets news sentiment analysis for a stock using Tiingo News API or LLM analysis, specifying the resource (stock) and action (get news sentiment). It distinguishes itself from siblings through its emphasis on enhanced reliability and fallback mechanisms.

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

Usage Guidelines3/5

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

The description explains reliability features and fallback options, but does not explicitly state when to use this tool versus alternatives like get_news_sentiment or research_analyze_market_sentiment. It provides context about never failing due to missing API key, but lacks explicit usage guidance.

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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