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TipRanks

by com.tipranks
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get_investor_sentiment

Read-onlyIdempotent

Returns crowd / retail investor sentiment for a stock.

Args:
    ticker: Stock ticker (e.g. 'NVDA')

Returns JSON with these top-level keys:
  - investorStatsOverview: stats aggregated across ALL TipRanks
      portfolios that hold the ticker. Fields:
        * numberOfPortfolios: total active portfolios on the platform.
        * portfoliosHoldingStock: how many of them hold THIS ticker.
        * averageAllocation: average % allocation among holders (decimal).
        * percentOverLast30Days / percentOverLast7Days: change in the
          count of holders over the window (decimal; 0.013 = +1.3%).
        * investorScore: TipRanks' 0-1 score of how confident "the
          crowd" is on this stock; higher = more bullish positioning.
        * sectorAverageScore: investorScore averaged across the sector,
          for comparison.
        * sentiment: bucketed label — one of "VeryNegative",
          "Negative", "Neutral", "Positive", "VeryPositive".
        * sectorAverageSentiment: same bucket, sector-wide.
  - bestInvestorStatsOverview: same fields, but restricted to "Best
      Investors" — TipRanks users with top-decile portfolio returns
      over the trailing window. If investorStatsOverview and
      bestInvestorStatsOverview diverge (e.g. crowd is Positive but
      best investors are Negative), that's the headline signal.
  - ageDistribution: holders split by TipRanks account-tenure tier
      (NOT the investor's biological age):
        * eldest: oldest accounts on the platform
        * midRange: middle tier
        * youngest: newest accounts
      Each has percentHolders, last30DaysChange, last7DaysChange,
      and per-bucket averages (averageBeta, averageMonthlyReturn,
      dividendYield, averagePeRatio).
  - investorsAlsoBought: top other stocks held by people who hold
      this one (each: ticker, companyName, averageHoldingSize,
      lastSevenDayChange, lastThirtyDayChange, sector, sectorName,
      score, sentiment, marketCap, marketCapCurrencyCode).
  - investorsAlsoBoughtYoungest / MidRange / Eldest: same shape,
      filtered to that account-tenure bucket. Often shorter or
      empty for stocks held mostly by one cohort.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "title": "Result",
-      "type": "string"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "title": "get_investor_sentimentOutput",
-  "type": "object"
-}New value: +null
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds substantial behavioral context beyond that: it explains the meaning of decimal fields (0.013 = +1.3%), clarifies that ageDistribution refers to account-tenure tiers not biological age, and highlights the divergence between crowd and best-investor sentiment as a headline signal. This exceeds the annotation baseline.

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 long but well-organized with clear section headers (investorStatsOverview, bestInvestorStatsOverview, ageDistribution, investorsAlsoBought). Given that no output schema is present, the detailed breakdown of return fields is justified and each section earns its place. It is front-loaded with the core purpose and then follows a logical structure.

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?

For a tool with no output schema and a complex nested JSON response, the description is remarkably complete. It documents every top-level key, explains field semantics (including percentage decimal interpretation), notes edge cases (empty lists for cohort-specific holdings), and provides interpretation guidance (the divergence signal). This gives an agent everything it needs to understand the tool's output.

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 only a 'ticker' string with no description. The description compensates by explicitly stating 'ticker: Stock ticker (e.g. NVDA)', providing an example and clarifying the expected format. Since schema coverage is 0%, the description adds critical meaning that would otherwise be missing.

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 opens with a clear, specific statement: 'Returns crowd / retail investor sentiment for a stock.' It identifies the resource (investor sentiment) and the action (returns), and distinguishes it from sibling sentiment tools like get_blogger_sentiment by focusing on TipRanks crowd/retail positioning.

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 implies when to use the tool—when you need crowd/retail sentiment for a stock—but does not explicitly compare it to alternatives like get_blogger_sentiment or get_bulls_bears_summary. No exclusion or when-not-to-use guidance is provided, so it earns a middle score.

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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