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SEC Fundamentals API

by com.multyplylabs
OverviewDiscussionsAdmin

Standard Fundamental Scores

standard_score
Read-onlyIdempotent

Academic fundamental scores across US public companies, chosen by name: piotroski (F-Score 0-9, financial strength), altman_z (bankruptcy-risk zones), beneish_m (elevated M-Score: an accounting-quality screen, not evidence of manipulation), magic_formula (Greenblatt rank on earnings yield and return on capital) or accruals (Sloan earnings quality). Each company is scored on its own latest fiscal year (fiscal_year per row); fy selects the cohort whose latest scored year is fy -- it does not re-score companies on an earlier year. Use company_signal for one company's quality, value and sentiment factors together. Peer-reviewed formulas; not investment advice. Cost: $0.15 per call; unpaid calls return a payment-required error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fyNoCohort to return: companies whose latest scored fiscal year is fy (they are not re-scored on an earlier year). Omit for every company on its own latest year. A year no company is scored on returns an error listing the available years.
nameYesScore to rank by: piotroski, altman_z, beneish_m, magic_formula or accruals.
zoneNoaltman_z only: safe, grey or distress; omit for every zone, most distressed first.
limitNoMaximum companies to return, 1-200.
worstNoaccruals only: true ranks the highest accrual ratios (lowest earnings quality) first; omit or false ranks the lowest first.
min_scoreNopiotroski only: minimum F-Score, 0-9; omit for 7.
min_revenueNoMinimum annual revenue in USD, e.g. 1e9.
flagged_onlyNobeneish_m only: omit or true for elevated M-Scores only; false for every score.
exclude_foreignNoExclude foreign private issuers (20-F filers); set false to include them.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed9 schema fields changed
    • addedInput schema / properties / exclude_foreign / description
      Added value: +"Exclude foreign private issuers (20-F filers); set false to include them."
    • addedInput schema / properties / flagged_only / description
      Added value: +"beneish_m only: omit or true for elevated M-Scores only; false for every score."
    • addedInput schema / properties / fy / description
      Added value: +"Cohort to return: companies whose latest scored fiscal year is fy (they are not re-scored on an earlier year). Omit for every company on its own latest year. A year no company is scored on returns an error listing the available years."
    • addedInput schema / properties / limit / description
      Added value: +"Maximum companies to return, 1-200."
    • addedInput schema / properties / min_revenue / description
      Added value: +"Minimum annual revenue in USD, e.g. 1e9."
    • addedInput schema / properties / min_score / description
      Added value: +"piotroski only: minimum F-Score, 0-9; omit for 7."
    • addedInput schema / properties / name / description
      Added value: +"Score to rank by: piotroski, altman_z, beneish_m, magic_formula or accruals."
    • addedInput schema / properties / worst / description
      Added value: +"accruals only: true ranks the highest accrual ratios (lowest earnings quality) first; omit or false ranks the lowest first."
    • addedInput schema / properties / zone / description
      Added value: +"altman_z only: safe, grey or distress; omit for every zone, most distressed first."
  2. Changed1 schema field changed
    • addedInput schema / properties / fy
      Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Fy"
+}
  3. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent/non-destructive, and the description adds substantial behavior beyond that: fy selects the cohort but does NOT re-score companies, each row carries its own fiscal_year, beneish_m is 'an accounting-quality screen, not evidence of manipulation,' and the call costs $0.15 with a payment-required failure mode. Nothing contradicts the annotations.

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?

Roughly 135 words for a 9-parameter tool with 16 siblings — every clause earns its place and the core purpose is front-loaded. The score-type glossary, fy clarification, and cost disclosure are all functional, though the dense paragraph could be slightly restructured for easier scanning.

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 and parameter coverage is 100%, so the description needn't document return values or parameter formats — yet it still adds the missing glue: what each score means, the cohort/fiscal-year semantics, the alternative routing, and the cost/failure mode. An agent has everything needed to invoke the tool correctly.

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% so the baseline is 3, and the description clearly adds value above the schema: it glosses each name value (what an F-Score of 0-9 means, what altman_z zones indicate, why an elevated M-Score is not proof of manipulation) and reinforces fy's cohort semantics. It doesn't elaborate on the remaining eight parameters, but the schema already documents those thoroughly.

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?

Opens with a specific verb and resource — 'Academic fundamental scores across US public companies' — and enumerates all five scoring models (piotroski, altman_z, beneish_m, magic_formula, accruals) with one-line meanings (F-Score 0-9, bankruptcy-risk zones, Greenblatt rank, Sloan earnings quality). It also names the sibling company_signal as the alternative for single-company composite analysis, so an agent can select the right tool without opening schemas.

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?

Gives an explicit when-to-use: cohort-level fundamental scoring of US public companies, selected by model name. It gives an explicit when-not and alternative: 'Use company_signal for one company's quality, value and sentiment factors together.' The fy clarification also prevents the common misuse of expecting companies to be re-scored on earlier fiscal years.

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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