get_ai_predictionAI next-day prediction: probability the stock closes UP, a plain buy/watch/sell-lean signal, and how strongly the models agree (consensus).
Available to every authenticated plan (Free / Pro / Enterprise); subject
to the caller's plan requests/day and requests/minute limits.
Args:
ticker: Stock symbol, e.g. "TSLA".
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (read-only, idempotent, non-destructive), the description discloses plan-based request limits and the three output components, adding useful behavioral context. It does not contradict annotations and enriches the understanding of what the tool returns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise—two sentences plus a parameter note—and front-loaded with the core purpose. It avoids redundancy but could be slightly more structured by separating output details from access info; still, no filler exists.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description sufficiently covers the output types (probability, signal, consensus), the parameter, and access/rate-limit context. The return format is implied enough for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only the ticker's type and title; the description's Args section adds a concrete definition and example ('Stock symbol, e.g. "TSLA"'), giving the parameter meaning. Since schema coverage is 0%, this compensation is valuable, even if simple.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly identifies the tool as providing an AI next-day prediction with specific outputs (probability, buy/watch/sell signal, consensus), which distinguishes it from siblings like analyze_stock or get_monte_carlo. The verb 'prediction' and resource 'stock' are explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining next-day stock predictions, but does not explicitly state when to use this tool over alternatives or provide exclusions. It does add access context (available to all plans, rate limits), which helps but isn't direct tool-selection 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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Each tool targets a distinct purpose: individual quant analyses (AI prediction, IV, Monte Carlo, option pressure, equity curve, risk scan), aggregation (analyze_stock), output generation (images, report), and account registration. No two tools are likely to be confused.
All tool names follow a uniform verb_noun snake_case pattern (e.g., get_ai_prediction, generate_stock_images, register_account). The convention is applied consistently across the entire set.
10 tools is well within the ideal 3-15 range and covers the full stock-analysis workflow: data gathering, analysis, aggregation, and report generation. Each tool contributes distinct functionality without bloat.
The tool set comprehensively covers the domain of quant stock analysis: predictive models, backtesting, options/IV analysis, risk scanning, aggregated analysis, and visual/report outputs. No obvious dead ends or missing core operations for the intended purpose.