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CohenD

fin-data-mcp-server

by CohenD

SEC EDGAR XBRL frames (one fact across all companies)

sec_xbrl_frames
Read-onlyIdempotent

Aggregate a specific XBRL financial fact across all companies for a given calendar period (annual, quarterly, or instantaneous).

Instructions

Aggregates one XBRL fact, for one period, across ALL reporting companies — the cross-sectional complement to company_concept. Period is a calendar frame: annual durations 'CYyyyy' (e.g. CY2023), quarterly durations 'CYyyyyQn' (e.g. CY2023Q1), or instantaneous 'CYyyyyQnI' (e.g. CY2023Q1I, balance-sheet items). Example: { taxonomy: 'us-gaap', tag: 'AccountsPayableCurrent', unit: 'USD', period: 'CY2023Q1I' }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagYesXBRL tag, e.g. AccountsPayableCurrent, Revenues, Assets
unitNoUnit of measure, e.g. USD, shares, USD-per-sharesUSD
periodYesCalendar frame: CY2023 (annual), CY2023Q1 (quarterly), CY2023Q1I (instantaneous)
taxonomyNoe.g. us-gaap, dei, ifrs-fullus-gaap
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds period format details and an example but does not explain response structure or pagination. Adds moderate value beyond annotations.

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?

Description is two sentences plus an example, all highly relevant. No wasted words. Front-loaded with primary purpose.

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

Completeness3/5

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

No output schema exists, so description should hint at return values. It does not, though the example implies a fact value. Given low complexity and good parameter docs, it is minimally adequate but could describe result structure.

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 baseline is 3. Description provides a concrete example mapping parameters (taxonomy, tag, unit, period) and clarifies period format. This extra context raises the score.

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?

Clear verb 'aggregates' identifies the action, specifies 'one XBRL fact, for one period, across ALL reporting companies' and explicitly contrasts with sibling 'company_concept'. Period format is precisely defined with examples.

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?

States cross-sectional use case and contrasts with sibling, but does not explicitly exclude scenarios or mention prerequisites. The sibling list provides additional context for selection.

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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