Flexreport Finance

predict_earnings_move

Read-only

Predict how big a stock's move will be when it next reports earnings. For each symbol, returns
its next earnings date (today or later) and `expected_move`: the expected absolute move of the
reaction session as a log return (0.05 is about 5%; size only, no direction). For context,
`hist_move_mean` / `hist_move_n` give the stock's own average reaction over its past reports and
how many reports that covers. Symbols with no scheduled report come back in
`no_upcoming_earnings_symbols`; uncovered tickers in `not_supported_symbols`.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`symbols`	Yes	Tickers to score, e.g. ["AAPL", "NVDA"].

Output Schema

TableJSON Schema

Name	Required	Description
`predictions`	Yes	One prediction per covered symbol with a scheduled report.
`not_supported_symbols`	Yes	Requested tickers outside Flexreport coverage.
`no_upcoming_earnings_symbols`	Yes	Covered tickers with no scheduled report.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 15, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations only declare readOnlyHint, so the description carries most of the burden and does well: it explains the returned fields (`expected_move` units and semantics, `hist_move_mean`/`hist_move_n` scope) and the two partition buckets for no-data symbols. It doesn't mention latency, rate limits, or coverage limits on historical depth, keeping it short of a 5.

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?

Front-loaded with the core purpose, then scoped precisely around output fields and edge cases in a tight block. Slightly dense with backticked field names, but every sentence carries information and there is no padding.

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?

An output schema exists, so return-value explanation is optional, yet the description goes further by defining units, direction semantics, historical context fields, and edge-case buckets — everything an agent needs to interpret and route results correctly. No gaps remain for a single-parameter read tool.

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

Parameters4/5

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

Only one parameter with 100% schema description coverage, so the baseline is already high. The description adds value by clarifying that each symbol returns per-symbol results and what happens to symbols without scheduled earnings, which the schema alone does not convey.

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?

States a specific verb (predict) and resource (post-earnings move) with the exact metric being returned (`expected_move` as a log return, size only, no direction). No sibling tool does anything similar — the closest neighbors are earnings announcements listing (`list_earnings_announcements`) and generic stock data, and this is clearly distinguishable from both.

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

Usage Guidelines3/5

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

Usage context is implied by naming the input (symbols) and outputs, but there is no explicit 'use this when vs. list_earnings_announcements' guidance or exclusions. The description covers behavior thoroughly but never tells the agent when this is the right tool over alternatives.

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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