Quantral Stock Sentiment

Company signal strength

get_company_score

Read-onlyIdempotent

Returns one company's Quantral social-conversation signal score for the 24h and the 7d window, each with a tier word (Exceptional, Strong, Moderate, Weak, Negative), plus the latest explanation and price, and the chips the company page shows when they apply: the signal insight (for example Turned positive or Split views, with how many trusted voices lean each way today), insider buying in the last 30 days and high volatility. The score measures the strength and credibility of market conversation across tracked public sources; it is not a price prediction, price target or investment recommendation. A null score means no tracked mentions in that window, which is different from a low score.

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": "get_company_score"
  }
}

Input Schema

Name	Required	Description	Default
`ticker`	Yes	Ticker symbol, for example AAPL.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 10, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive, closed-world behavior, and the description still adds real value: null means no tracked mentions and is explicitly not equivalent to a low score, chips are conditional ('when they apply'), and the score is a conversation-strength measure rather than a price target. It stops short of disclosing freshness/latency or update cadence beyond the vague 'latest'.

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?

Front-loaded with the return contract, and the parenthetical tier list and chip examples are informative rather than filler. It is a dense single paragraph, though, and the chip enumeration could be tightened without losing meaning.

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?

With no output schema and only one obvious input, the description does the heavy lifting by describing return contents, tier vocabulary, conditional chips and null semantics. Combined with annotations covering the safety profile, an agent has everything needed to call and interpret this tool.

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?

There is a single required parameter with 100% schema description coverage ('Ticker symbol, for example AAPL.'), so the schema already carries the semantics. The description adds only that the ticker resolves to exactly one company, with no extra detail on symbol format or unresolvable-ticker behavior.

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 concrete verb and resource ('Returns one company's Quantral social-conversation signal score') and pins the scope to 24h and 7d windows, tier words, explanation, price and conditional chips. The single-company framing plus the enumerated payload clearly separates it from aggregate or list-oriented siblings such as get_industry_scores or get_company_signals.

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?

Usage is only implied: an agent can infer it is the tool for one company's score, but there is no explicit 'use this when…' statement and no named alternative for related needs (e.g., multi-company signals or industry-level scores). The disclaimer about not being a price prediction is interpretation guidance rather than routing 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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