agent-toolbelt

Stock Investment Thesis

stock_thesis

Create long-term investment theses for stocks by analyzing financials, valuation metrics, insider trades, and analyst ratings to generate comprehensive research notes with verdicts, strengths, risks, and valuation assessments.

Instructions

Generate a long-term investment thesis for any stock. Pulls live financials, valuation metrics, insider trades, and analyst ratings, then synthesizes them into a Motley Fool-style research note. Returns a bullish/neutral/bearish verdict, thesis paragraphs, key strengths, risks, and valuation read. Use when you want fundamental analysis of a stock for long-term investing.

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": "stock_thesis"
  }
}

Input Schema

Name	Required	Description	Default
`ticker`	Yes	Stock ticker symbol (e.g. NVDA, AAPL, MSFT)
`timeHorizon`	No	Investment time horizon	3-5 years

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Mar 30, 2026

TDQS

A4.2/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. Successfully discloses data sources ('live financials, valuation metrics, insider trades, analyst ratings'), processing style ('Motley Fool-style research note'), and output structure ('bullish/neutral/bearish verdict, thesis paragraphs...'). Missing only side-effect warnings or rate limit notes.

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?

Four sentences with zero waste: (1) Purpose, (2) Data sources & processing, (3) Output structure, (4) Usage guidance. Front-loaded and efficiently structured; every sentence earns its place.

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?

Despite no output schema, description explicitly documents return values (verdict categories, thesis components). Covers input semantics, behavioral traits, and output structure adequately for a 2-parameter synthesis tool. Minor gap: no mention of data freshness or caching behavior.

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%, establishing baseline 3. Description references 'long-term' and 'any stock' which loosely map to parameters, but adds no syntax, format details, or semantic constraints beyond the schema's 'e.g. NVDA, AAPL' example.

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?

Specific verb ('Generate') + resource ('long-term investment thesis') + scope ('any stock'). Distinguishes from siblings like 'earnings_analysis' (narrow focus), 'valuation_snapshot' (metrics only), and 'bear_vs_bull' (comparison format) by emphasizing comprehensive fundamental synthesis.

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?

Provides explicit positive guidance ('Use when you want fundamental analysis of a stock for long-term investing') with clear context. Lacks explicit negative constraints or named alternatives (e.g., when to use 'earnings_analysis' instead), preventing a 5.

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