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 cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is lower. The description adds genuine unstated context: the output is research only and not a trade instruction, plus plan gating and rate-limit behaviour.
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 lead sentence front-loads the purpose and return contents, and the availability caveat follows compactly. The 'Args:' block is slightly redundant given the one-parameter schema, but nothing is bloated.
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?
Though there is no output schema, the description enumerates what comes back (probability, lean, consensus), which compensates. The disclaimer and rate-limit note round out what an agent needs before invoking; the only gap is no guidance on alternatives.
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?
Only one parameter, and the schema description coverage is 0%, so the description must carry the burden. It does name the parameter and supply a format example ('TSLA'), which is enough meaning for a single ticker argument.
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 resource and scope: an AI next-day prediction consisting of the probability the stock closes UP, a paper-trading lean, and model consensus. This is clearly distinguishable from siblings like get_monte_carlo or get_iv_radar without opening any schema.
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?
It states availability ('every authenticated plan') and that it is subject to per-day and per-minute rate limits, which is useful operating context. However it never says when to prefer this over siblings such as analyze_stock or get_iv_radar, and gives no exclusions.
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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