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SEC EDGAR Fundamentals

Get One Metric's Reported Series

get_concept

The reported time series for a single normalized metric from one company's XBRL facts — every revenue, net_income or assets observation the filer has published, each with its value, unit, period start and end, and the filing it came from. Use it when a question is about the history, trend or growth of one figure rather than a whole statement, or when you must reconcile a number against the filing it was tagged in. Returns the observations newest first alongside the upstream tag and taxonomy that actually resolved, which matters because filers tag the same economic concept differently and a series cannot be compared across companies without knowing which tag answered. See /.well-known/x402 for current pricing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many observations to return, newest first. Accepts 1 to 200; defaults to 20 when omitted. A decade of annual history needs about 10.
metricNoNormalized metric key to fetch, exactly as published by list_metrics — for instance revenue, net_income, assets, equity or operating_cash_flow. Call list_metrics first if unsure; a key outside that vocabulary is rejected.
tickerYesExchange ticker symbol of a single SEC filer, case-insensitive, such as AAPL, MSFT or KO.
taxonomyNoXBRL taxonomy the resolved tag is drawn from: 'us-gaap' for financial statement concepts, 'dei' for entity-level facts such as shares outstanding. Defaults to 'us-gaap'.us-gaap

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses newest-first ordering, that results include the upstream tag and taxonomy actually resolved, and the caveat about comparing series across companies. It does not cover error behavior or rate limits, but the core behavioral traits are well explained.

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 dense and well-structured: output description, usage guidance, behavioral nuance, and pricing link. It is slightly long with em-dash clauses, but every sentence contributes value without redundancy.

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?

Without an output schema, the description fully explains what is returned (value, unit, period start/end, filing, tag, taxonomy), the ordering, and the importance of the resolved tag. It also references pricing. This covers all essential context for an agent to decide and correctly invoke the tool.

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

Parameters3/5

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

Schema description coverage is 100% and the schema already fully documents parameters. The description adds examples like 'revenue, net_income or assets' and mentions 'limit' indirectly through history depth, but does not provide substantial parameter-level meaning beyond the schema, so baseline 3 is appropriate.

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 a reported time series for a single normalized metric from one company's XBRL facts, with concrete examples (revenue, net_income, assets) and details on included fields. It distinguishes itself from siblings by emphasizing 'one figure rather than a whole statement'.

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?

Explicitly states when to use: 'when a question is about the history, trend or growth of one figure' or 'when you must reconcile a number against the filing it was tagged in.' It contrasts with 'a whole statement' but does not name specific alternative tools, so it stops short of a 5.

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

A4.3/5.0
Disambiguation5/5

Every tool targets a distinct resource and action: company reference, financial statements, single-metric history, cross-company comparison, filing history, insider transactions, insider aggregation, and vocabulary lookup. Cross-references between insider tools make boundaries explicit and prevent misselection.

Naming Consistency4/5

Names are all snake_case and mostly follow predictable verb_noun conventions like get_*, list_*, and screen_*. A few noun-phrase names (company_profile, insider_profile, issuer_insider_activity) and adjective-noun names (latest_insider_filings) break the strict pattern, but the meaning remains clear.

Tool Count5/5

Twelve tools is well within the ideal range for a focused SEC EDGAR fundamentals server. Each tool covers a meaningful slice of either financial data or insider ownership data, and none feels redundant or unnecessary.

Completeness4/5

The server covers the core read-only workflows well: company identity, normalized financials, metric history, cross-company comparison, filing history, insider transactions, insider profiles, and vocabulary support. Minor gaps exist—no company search by name/CIK and no raw filing text retrieval—but common fundamental and insider questions can be answered without dead ends.

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