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SEC Fundamentals API

by com.multyplylabs
OverviewDiscussionsAdmin

Buffett Value Scan

buffett_value
Read-onlyIdempotent

Find high-quality US public companies trading below intrinsic value: the Quality Score (see buffett_scan) combined with a value score from the previous close (FCF yield, 10-year owner-earnings DCF margin of safety, P/E), ranked by combined_score, the mean of the two (0-100 each). Rows also carry market cap, owner-earnings yield and intrinsic value. Use buffett_scan for quality alone; it needs no price data. Foreign private issuers (20-F filers, e.g. Chinese ADRs) are excluded by default: they screen cheap for structural reasons fundamentals cannot see. Methodology v3; heuristic screen, not investment advice. Cost: $0.25 per call; unpaid calls return a payment-required error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fyNoFiscal year of the published Quality Scores; omit for the latest. A year that isn't published returns an error listing the published years.
limitNoMaximum companies to return, 1-200.
industryNo2-digit SIC industry group, e.g. 73 (business services, including software); omit for every industry.
min_scoreNoMinimum combined_score (the mean of the quality and value scores), 0-100.
min_revenueNoMinimum annual revenue in USD, e.g. 1e9.
exclude_foreignNoExclude foreign private issuers (20-F filers); set false to include them.
min_margin_of_safetyNoMinimum DCF margin of safety (1 - market cap / intrinsic value), e.g. 0.2 for a price 20% below intrinsic value; 0 keeps companies at or below intrinsic value.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed7 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 / fy / description
      Added value: +"Fiscal year of the published Quality Scores; omit for the latest. A year that isn't published returns an error listing the published years."
    • addedInput schema / properties / industry / description
      Added value: +"2-digit SIC industry group, e.g. 73 (business services, including software); omit for every industry."
    • addedInput schema / properties / limit / description
      Added value: +"Maximum companies to return, 1-200."
    • addedInput schema / properties / min_margin_of_safety / description
      Added value: +"Minimum DCF margin of safety (1 - market cap / intrinsic value), e.g. 0.2 for a price 20% below intrinsic value; 0 keeps companies at or below intrinsic value."
    • addedInput schema / properties / min_revenue / description
      Added value: +"Minimum annual revenue in USD, e.g. 1e9."
    • addedInput schema / properties / min_score / description
      Added value: +"Minimum combined_score (the mean of the quality and value scores), 0-100."
  2. Changed3 schema fields changed
    • addedInput schema / properties / fy / anyOf
      Added value: +[
+  {
+    "type": "integer"
+  },
+  {
+    "type": "null"
+  }
+]
    • changedInput schema / properties / fy / default
      Previous value: -2025New value: +null
    • removedInput schema / properties / fy / type
      Removed value: -"integer"
  3. First observed

TDQS

A4.9/5.0
Behavior5/5

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

The description adds substantial behavioral context beyond the annotations: paid per call, payment-required error behavior, default exclusion of 20-F filers, methodology version, and a disclaimer that it is a heuristic screen, not investment advice. This goes well beyond the readOnly and idempotent hints already provided.

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 dense but every sentence earns its place: purpose, scoring detail, output fields, alternative tool, exclusions, methodology caveat, and cost. The most important information is front-loaded, and it remains concise given the tool's complexity.

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 tool with seven optional parameters and an output schema, the description is remarkably complete. It covers purpose, ranking, output contents, when to use an alternative, default filtering behavior, methodology, cost, and error conditions. There is no meaningful gap that would leave an agent unsure how to invoke it 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 parameters are already well documented. The description adds useful conceptual context by explaining that combined_score is the mean of quality and value scores supplies and identifying value score components (FCF yield, DCF margin of safety, P/E), which improves understanding of parameters like min_score and min_margin_of_safety.

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 states a specific verb ('Find'), a clear resource ('high-quality US public companies trading below intrinsic value'), and explains the scoring mechanism. It also differentiates itself from buffett_scan by explicitly combining quality and value scores, so an agent can distinguish the two tools.

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?

The description explicitly tells the agent when to use the sibling buffett_scan instead ('quality alone; it needs no price data') and clarifies the default exclusion of foreign private issuers. It also mentions the cost and payment-required error, giving practical usage context.

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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