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get_earnings_call_summary

Read-onlyIdempotent

Returns the most recent earnings call summary for a ticker — management guidance text, overall call sentiment (positive / neutral / negative with a one-line rationale), and AI-extracted highlights and lowlights from the call as {title, content} bullets.

This is a structured summary derived from the call, not the raw
transcript text. Useful for "what did management say about X on the
last call", "was the most recent call bullish or bearish", or
"summarise the highlights from MSFT's latest earnings".

Only the most recent quarter is stored per ticker; for historical
EPS actual-vs-estimate series use get_earnings_history.

Args:
    ticker: Stock ticker (e.g. 'AAPL', 'NVDA').

Returns:
    { ticker, fiscal_year, fiscal_quarter, guidance,
      sentiment: { label, summary },
      highlights: [ { title, content }, ... ],
      lowlights:  [ { title, content }, ... ] }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "title": "Result",
-      "type": "string"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "title": "get_earnings_call_summaryOutput",
-  "type": "object"
-}New value: +null
  2. First observed

TDQS

A4.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds beyond these by noting it's a structured summary (not raw transcript), that only the most recent quarter is stored, and that highlights are 'AI-extracted', implying potential unreliability. This is useful context but doesn't cover failure modes or rate limits.

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

Conciseness5/5

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

The description is front-loaded with the core purpose and each sentence adds value: content, usage guidance, sibling differentiation, and parameter/return details. No filler or tautology; structured with Args and Returns for clarity.

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

Completeness5/5

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

For a single-parameter, read-only tool, the description is complete: it explains input, output format, data scope, and limitations (only most recent quarter). It even provides the return JSON structure, so no separate output schema is needed.

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

Parameters5/5

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

Schema coverage is 0%, so the description fully compensates. The Args section explains 'ticker' with concrete examples ('AAPL', 'NVDA') and clearly states it's a stock ticker. The return structure is also described, making the parameter's purpose unambiguous.

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

Purpose5/5

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

The description clearly states the tool returns the most recent earnings call summary for a ticker, enumerating specific components (guidance, sentiment, highlights, lowlights). It distinguishes itself from raw transcript and sibling tools like get_earnings_history.

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

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit use cases are provided (e.g., 'what did management say about X', 'was the call bullish or bearish'). It directly names an alternative (get_earnings_history) for historical EPS series, giving clear when-to-use vs. when-not-to guidance.

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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