get_ai_predictionAI next-day prediction: probability the stock closes UP, a paper-trading buy/watch/sell lean (research only, not a trade instruction), 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 |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint and non-destructive; the description adds value beyond that by disclosing the research-only disclaimer, per-plan quota/rate-limit behavior, and the three output signals. It stops short of 5 only because it doesn't say how much history/latency to expect or how stale the prediction may be.
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?
Front-loads what the tool returns, then availability/limits, then the argument — a sensible order with no filler. The Args block is slightly redundant with the schema but is short enough not to waste space.
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?
With no output schema, the description correctly takes on the job of explaining the three returned signals, and it also covers plan eligibility, rate limits, and the non-advisory disclaimer. An agent has everything needed to call it and to interpret the result.
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?
Schema coverage is 0% (ticker has no description in the schema), so the description must carry the load, and it does: it names the parameter, states 'Stock symbol', and gives a concrete example ("TSLA"). Missing format details (exchange suffix, case sensitivity, unsupported symbols) keep it from a 5.
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?
States a specific verb+resource — an AI next-day direction prediction for a ticker — and enumerates exactly what the answer contains (UP probability, buy/watch/sell lean, model consensus). It is clearly distinct from siblings like get_monte_carlo or get_equity_curve in substance, but never names a sibling to contrast with, so it stops short of a 5.
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?
Implies when to use it (next-day directional forecast) and warns 'research only, not a trade instruction', plus states plan availability and rate-limit exposure. However, there is no explicit guidance on how it differs in use from analyze_stock or get_monte_carlo — the agent must infer which forecasting tool to pick.
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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