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 |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -{
- "properties": {
- "result": {
- "title": "Result",
- "type": "string"
- }
- },
- "required": [
- "result"
- ],
- "title": "get_monte_carloOutput",
- "type": "object"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds behavioral context by explaining the Monte Carlo methodology, the probabilistic nature of thousands of random paths, and the ~10 trading day horizon, which is not covered by 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 and front-loaded: the first sentence states the core purpose, and the second section succinctly documents the parameter. Every sentence earns its place with no 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-parameter read-only simulation, the description covers purpose, time horizon, and the concept of the output (price range and odds). It lacks explicit output format details, but this is not critical given the tool's simplicity and absence of an output schema.
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 input schema has 0% description coverage for the ticker parameter. The description compensates fully with an 'Args' section explaining ticker as a stock symbol with an example ('AAPL'), adding meaning beyond the bare schema.
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 it performs Monte Carlo price simulation for the next ~10 trading days, estimating a price range and odds of finishing higher. This specific verb+resource+scope distinguishes it from siblings like get_ai_prediction and get_iv_radar.
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 provides clear context on the tool's use case (price range and odds estimation for a ~10 day horizon), but does not explicitly mention when to use it over alternatives or any exclusions. This fits the 'clear context, no exclusions' level.
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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