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LiveDataLink

by io.github.blackboxfoundry

edgar_company_facts

Read-onlyIdempotent

Get structured XBRL financial facts for a company. Without 'concept', returns the top-level facts catalog (concepts the company has reported). With 'concept' (e.g. 'Revenues', 'Assets', 'EarningsPerShareBasic'), returns the time series of values for that concept.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conceptNoOptional XBRL concept name (e.g. 'Revenues', 'Assets', 'NetIncomeLoss'). If omitted, returns the catalog of available concepts.
taxonomyNoOptional XBRL taxonomy (default 'us-gaap').
identifierYesTicker symbol or CIK.

TDQS

A4.4/5.0
Behavior4/5

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

The description discloses the key mode-dependent behavior—catalog vs. time series—beyond the safety annotations. With readOnlyHint and idempotentHint already declared, the main added value is explaining the two output modes and the effect of 'concept', which it does well.

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?

Two sentences with no filler. The primary purpose is front-loaded, and the mode distinction is stated compactly with useful examples.

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

Completeness4/5

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

For a three-parameter tool with no output schema, the description covers both invocation modes and the meaning of the returned data. It could add specifics about default taxonomy behavior or units/format of the time series, but the core agent decision of which mode to call is fully supported.

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; the description adds value by explaining that supplying 'concept' produces the time series of values and giving concrete examples like 'Revenues' and 'EarningsPerShareBasic'. This enriches the schema's minimal statement that 'concept' is an XBRL concept name.

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 leads with a specific verb and resource: 'Get structured XBRL financial facts for a company,' which clearly distinguishes it from sibling EDGAR tools like full-text search or filing content. It then specifies the two output modes depending on whether 'concept' is supplied, making the tool's purpose unambiguous.

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?

It gives clear conditional usage within the tool: omit 'concept' for the catalog, include it for a concept's time series. It does not explicitly name alternatives among the sibling EDGAR tools or state when not to use this tool, so it stops short of full differentiation.

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

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources