Returns upcoming earnings dates across all stocks for a date range.
Args:
fromDate: Start date YYYY-MM-DD (default: today)
toDate: End date YYYY-MM-DD (default: 14 days from now)
exchange: Optional exchange filter, e.g. 'US' for US-only results
| Name | Required | Description | Default |
|---|---|---|---|
| toDate | No | ||
| exchange | No | ||
| fromDate | No |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -{
- "properties": {
- "result": {
- "title": "Result",
- "type": "string"
- }
- },
- "required": [
- "result"
- ],
- "title": "get_earnings_calendarOutput",
- "type": "object"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, which cover safety. The description adds the date default behavior (today to 14 days) but does not disclose additional traits like rate limits, data freshness, or pagination. With annotations present, this is adequate but not extra-rich.
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 two lines plus a concise argument list. The first sentence states the purpose, and the argument list is neatly formatted with defaults and examples. No word wasted.
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?
Despite no output schema, the description tells the agent what to expect: 'upcoming earnings dates'. For a simple filtered-list read-only tool with good annotations, this is sufficient context. The date defaults and exchange filter are documented, making the tool complete.
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 description coverage is 0%, so the description carries full responsibility. It explains each parameter: fromDate (format YYYY-MM-DD, default today), toDate (format, default 14 days), and exchange (optional, with example 'US'). This fully compensates for the schema gap.
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 returns 'upcoming earnings dates across all stocks for a date range', using a specific verb and resource. It distinguishes from sibling tools like get_ticker_earnings (which is likely stock-specific) and get_economic_calendar (which covers macro events), so purpose clarity is high.
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 scope context ('across all stocks', date range) and the optional exchange filter, implying when to use it. However, it does not explicitly name alternative tools or state when not to use it, earning a 4 rather than a 5.
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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