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LastLook Data

by io.github.zev-lll

Get Company Fundamentals (SEC EDGAR)

get_edgar_company
Read-onlyIdempotent

Returns financial fundamentals for any US public company from SEC EDGAR XBRL filings: revenue, net income, total assets, stockholders equity, and EPS. Includes both annual (10-K) and quarterly (10-Q) data for the most recent periods. Works for any ticker listed on a US exchange (AAPL, MSFT, TSLA, AMZN, NVDA, GOOGL, META, JPM, BAC, etc.). Source: SEC EDGAR. Priced at $0.75 USDC via x402 on Base.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker symbol e.g. AAPL, MSFT, TSLA, AMZN, NVDA, GOOGL

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cikYesSEC Central Index Key
as_ofYesDate the data was fetched
tickerYesTicker symbol
edgar_urlYesEDGAR filing browser URL for this company
company_nameYesCompany legal name
fundamentalsYesFinancial data: revenue, net_income, total_assets, stockholders_equity, eps_basic

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond these annotations, including that it covers annual (10-K) and quarterly (10-Q) data, the source is SEC EDGAR, and it is priced at $0.75 USDC via x402 on Base. No contradiction with 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?

The description is slightly longer than minimal but every sentence earns its place: it provides the main purpose, the specific metrics, the data frequency, the scope with examples, the source, and the pricing. It is front-loaded with the primary function and maintains a logical flow.

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?

Given the tool is simple (single parameter) and an output schema exists (so return structure is defined), the description covers all essential aspects: purpose, scope, data types, frequency, source, and cost. It is complete for an agent to select and 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?

The schema already describes the ticker parameter with examples at 100% coverage, giving a baseline of 3. The description adds meaning by clarifying that the ticker must be listed on a US exchange and by expanding the example list, which helps the agent understand the accepted input scope beyond the schema's brief description.

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 financial fundamentals for any US public company from SEC EDGAR XBRL filings' and lists the specific metrics (revenue, net income, etc.), distinguishing it from sibling tools that focus on macro, crypto, or FX bundles. The verb 'Returns' is specific and the resource is well-defined.

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 provides clear context for when to use the tool: for any US public company ticker, with examples like AAPL, MSFT, TSLA. However, it does not explicitly mention when not to use it or recommend alternative tools among the siblings, so it earns a 4 rather than 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

A3.9/5.0
Disambiguation3/5

The tool set has several overlapping functions, particularly among the bundle tools (e.g., get_bundle_context_brief vs get_bundle_macro both provide macro indicators, and get_bundle_rate_environment overlaps with get_yield_curve and get_policy_spread). Individual current/history/date tools are distinct but some redundancy exists (e.g., get_treasury_yield_current vs get_current_value for DGS30). Descriptions help differentiate purposes, but agents may still hesitate when selecting between similar bundles.

Naming Consistency4/5

All tool names begin with the verb 'get_' and use snake_case, creating a consistent pattern. The bundle tools are uniformly prefixed with 'get_bundle_', and individual data tools follow a get_[entity]_[modifier] structure (e.g., get_fx_rate_current, get_fx_rate_by_date, get_fx_rate_series). Minor deviations exist (e.g., get_series, get_current_value, get_value_by_date are less descriptive of the underlying entity), but overall the naming is predictable and readable.

Tool Count3/5

With 24 tools, the server is on the heavy side of the typical range. The broad domain (macro data, mortgage, crypto, FX, treasury, EDGAR) justifies many tools, but some could potentially be consolidated (e.g., individual rate tools vs rate bundles). The count does not feel overwhelming, but it is borderline heavy.

Completeness4/5

The server covers a wide range of economic and financial data with both bundled and granular views. It includes current, historical, and date-specific retrievals for FRED series, FX, and crypto, plus specialized tools for mortgages, recession, and yield curves. Minor gaps exist, such as no way to list all supported FRED series or full financial statements for EDGAR, but the core lifecycle of data querying is well covered.

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