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?
Annotations already indicate readOnly and idempotent hints. The description adds behavioral context that the prediction is for the next day and includes components like probability and signal, which is beyond what annotations provide. No contradiction with annotations.
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 with three focused sentences: purpose, availability, and parameter definition. Front-loaded with the core purpose, no unnecessary text.
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 simple tool with one parameter and no output schema, the description covers the purpose, output components, availability, and parameter definition. It is complete enough for an agent to understand when and how to invoke the tool.
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 single 'ticker' parameter is only a string in the schema. The description adds full semantics by defining it as 'Stock symbol' and providing an example 'TSLA', which compensates for the 0% schema description coverage.
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?
The description clearly states the tool provides an AI next-day prediction with specific outputs: probability of closing UP, a buy/watch/sell-lean signal, and consensus strength. This distinguishes it from siblings like analyze_stock which are broader.
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 explicitly states the tool is available to all authenticated plans and subject to rate limits, providing important context for the agent to decide if it can call the tool. While it does not explicitly compare to siblings, the availability and limits information is sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Every tool has a clearly distinct purpose, from individual analytics like AI prediction and IV radar to aggregation and report generation. No overlapping functionality.
All tool names follow a consistent verb_noun pattern using snake_case, with verbs like get_, generate_, and analyze_. No mixing of conventions.
9 tools is well-scoped for a quant finance server, covering diverse analytics and outputs without being excessive or sparse.
Covers major quant finance analyses and reporting. Minor gap: no raw data access tools, but the surface is comprehensive for the intended use case.