US Stocks & Market Data

get_nasdaq_fear_greed

Read-onlyIdempotent

[US Stock Info] Get a NASDAQ fear & greed score (0 = extreme fear, 100 = extreme greed) with its six components. Use this for questions about market sentiment, e.g. "is the market fearful or greedy now", "NASDAQ fear and greed index". This is not CNN's Fear & Greed Index. It is the utils.im model, computed from NASDAQ 100 (QQQ) daily closes: momentum, short-term trend, 52-week range position, volatility, RSI and share of up days. Takes no arguments.

Returns a JSON object:
    score, band: the latest score and its band (extremeFear, fear, neutral, greed, extremeGreed).
    components: each component's 0-100 score and raw value.
    compare: the score 1, 5, 21 and 250 trading days earlier.
    methodology_url: the utils.im page that documents this calculation and shows the 1-year history chart.

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 24, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare it as read-only, idempotent, non-destructive, and open-world. The description adds meaningful context beyond that: the exact data source (QQQ daily closes), the six components, the methodology URL, and the comparison lookback windows.

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 front-loaded with purpose and usage before details, and it remains scannable. The return-value section partly duplicates the available output schema, so it is not maximally concise.

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

Completeness5/5

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

Given the output schema and annotations, the description supplies enough context about what the score means, how it is computed, and what it returns. No critical information for correct invocation is missing.

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?

There are no parameters, and the description explicitly says it takes no arguments, which matches the empty input schema. For a zero-parameter tool this is appropriately complete.

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?

States a specific verb and resource (get NASDAQ fear & greed score), defines the 0-100 scale, and explains the underlying model and data source. It also distinguishes itself from CNN's similarly named index, so an agent can identify it precisely.

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?

Gives clear example user questions and states the tool takes no arguments, which helps an agent know when to invoke it. It does not explicitly compare against sibling tools, but the market-sentiment context is clear.

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

All US Stocks & Market Data tools Inspect

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