get_monte_carloMonte Carlo price simulation for the next ~10 trading days: thousands of random price paths estimate a likely price range and the odds of finishing higher.
Args:
ticker: Stock symbol, e.g. "AAPL".
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds context about the simulation method and output, but does not disclose potential limitations or data dependencies.
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: a single paragraph front-loading the purpose followed by a minimal parameter definition. Every sentence adds value without redundancy.
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, one-param tool with complete annotations and no output schema, the description adequately explains the simulation's time horizon, method, and expected outputs (price range, odds). Missing details on return format, but still functional.
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?
With 0% schema description coverage, the description compensates by specifying 'Stock symbol, e.g. 'AAPL',' adding meaningful context that the schema alone ('Ticker') lacks.
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 'Monte Carlo price simulation for the next ~10 trading days' and explains the method and outputs, effectively distinguishing it from siblings like 'analyze_stock' or 'get_ai_prediction'.
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?
No guidance on when to use this tool versus alternatives, such as when a simple price estimate suffices or when other prediction tools are more suitable.
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.