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robinhood-mcp

robinhood-mcp

by drasticstatic
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

robinhood_get_earnings

Get earnings data for any stock using its ticker symbol. View revenue, EPS, and earnings dates to support investment research and portfolio analysis.

Instructions

Get earnings data for a stock.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "symbol": "<symbol>"
    },
    "name": "robinhood_get_earnings"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYesStock ticker symbol

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits but only states the basic purpose. It does not mention whether results are filtered by date, whether it returns future or past earnings, or any side effects, leaving significant gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence is concise and front-loaded, but could incorporate more useful information within its brevity without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the existence of an output schema (not shown), the description does not need to detail return values. However, it lacks context about the nature of earnings data (e.g., quarterly vs annual) and any filtering capabilities, making it minimally complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single parameter 'symbol', with a clear description 'Stock ticker symbol'. The description adds no further meaning beyond the schema, achieving baseline adequacy.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states verb 'Get' and resource 'earnings data' for a stock, distinguishing from siblings like get_quote or get_news. However, it does not specify what aspects of earnings (e.g., revenue, EPS, date) are included, which could be more precise.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 like get_fundamentals which might also include earnings. No exclusions or context provided, leaving the agent without decision support.

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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