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trading-mcp-server

trading-mcp-server

by mukul8896
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_market_sentiment

Assess overall market mood by analyzing news sentiment. Returns bullish/bearish/neutral tally, active themes, and representative headlines to guide top-down market analysis.

Instructions

Overall market sentiment and trends from the whole news feed. Returns a sentiment tally + percentages, an overall mood (bullish|bearish|neutral), the most active themes (sub-categories), and representative positive/ negative headlines. Use this for a top-down read of market mood before drilling into a specific symbol.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_market_sentiment"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.1

TDQS

A3.7/5.0
Behavior3/5

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

No annotations provided. The description describes outputs but does not disclose behavioral traits like data freshness, rate limits, or side effects. Since it is a read-only tool with zero parameters, the omission is acceptable but not fully transparent.

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?

Two sentences: first covers functionality and outputs, second provides usage guidance. No fluff, front-loaded, and every sentence adds value.

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?

For a simple tool with no parameters and no output schema, the description explains purpose, outputs, and usage. It does not differentiate from sibling tools like 'analyze_news_sentiment' or 'get_sector_sentiment', leaving some ambiguity. Still, it is largely complete.

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 zero parameters with 100% coverage. According to guidelines, baseline is 4. The description adds no parameter info, which is appropriate since there are no parameters to document.

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 returns overall market sentiment from the news feed, specifying the outputs: tally, percentages, mood, themes, and headlines. It implies a top-level view, distinguishing it from symbol-specific tools, but does not explicitly differentiate from similar siblings like 'analyze_news_sentiment'.

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?

States 'Use this for a top-down read of market mood before drilling into a specific symbol', giving context for when to use. However, it does not mention when to avoid or provide alternative tools, leaving the agent to infer usage boundaries.

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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