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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Earnings Signals

get_earnings_signals
Read-onlyIdempotent

Reported earnings results and a model-derived earnings-trend signal for a company, by fiscal period: actual reported EPS, a trailing-trend EPS estimate (eps_trend_est), the deviation of actual vs that trend (eps_surprise_pct), reported revenue, and year-over-year revenue growth. IMPORTANT: eps_trend_est is NOT Wall Street analyst consensus — Valuein is sourced purely from SEC EDGAR and carries no consensus feed. It is a deterministic estimate computed from the company's own prior reported EPS, so eps_surprise_pct measures how far the print landed from its own trailing trend, not whether it 'beat the Street'. Use it to track earnings/revenue trajectory and momentum, not to claim a consensus beat or miss. Point-in-time safe — pass as_of_date to filter by SEC acceptance (accepted_at) for look-ahead-free backtests. Available on all plans.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of periods to return (1–40), most recent first. Defaults to 8 — covers 2 years of quarterly signals plus their TTM equivalents. earnings_signals.parquet currently emits one row per (entity, period_end); older rows surface here as more historical periods are published.
tickerYesStock ticker symbol, e.g. AAPL, MSFT
as_of_dateNoPoint-in-time filter: only return signals with accepted_at on or before this date. Use for backtesting to avoid look-ahead bias.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
noteYes
planYes
_metaYesProvenance envelope — data lineage for every MCP response
tickerYes
as_of_dateNo
estimate_basisYes
periods_returnedYes

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint and idempotentHint. The description adds that the tool is point-in-time safe for backtesting and explains that eps_trend_est is a deterministic estimate from SEC filings, not consensus. This provides behavioral context beyond annotations without contradiction.

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?

The description is a single paragraph that is information-dense but well-structured, front-loading the purpose and then detailing nuances. While slightly long, every sentence adds value, though bullet points could improve scanability.

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

Completeness4/5

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

Given the tool's complexity (3 parameters, output schema exists), the description adequately covers usage context, including point-in-time safety and the nature of the estimate. It does not explain the output schema, but that is likely covered elsewhere.

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?

Schema coverage is 100%. The description adds value by explaining the meaning of eps_trend_est and eps_surprise_pct, which are not fully defined in parameter descriptions. It also clarifies the default and behavior of limit, and the purpose of as_of_date for backtesting.

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 reported earnings results and a model-derived earnings-trend signal for a company by fiscal period. It specifies key fields (actual EPS, eps_trend_est, eps_surprise_pct, revenue, yoy growth), and distinguishes itself from Wall Street consensus tools, adding clarity.

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

Usage Guidelines4/5

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

The description advises using the tool to track earnings/revenue trajectory and momentum, not to claim consensus beats or misses. It also explains the nature of eps_trend_est and provides backtesting guidance with as_of_date. Though it doesn't list alternatives, it gives clear contextual usage 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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TDQS

A4/5.0
Disambiguation5/5

Each tool has a distinct purpose with detailed descriptions that clarify differences. Overlaps like get_peer_comparables vs screen_universe are well-differentiated by scope (single company vs cross-sectional). Similarly, get_insider_sentiment vs get_smart_money_flow are clearly distinguished by data sources and methodology.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., create_report, get_financial_ratios, delete_alert). No mixing of conventions or inconsistent verbs.

Tool Count2/5

With 69 tools, the count far exceeds the 25+ threshold for 'too many'. While the domain is broad, the sheer volume likely overwhelms agents and increases selection complexity.

Completeness4/5

The tool set covers a wide range of SEC filings, ratios, smart-money data, alerts, reports, and more. Minor gaps exist (e.g., no options or detailed debt data), but most analyst workflows are supported.

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