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LiveDataLink

by io.github.blackboxfoundry

census_demographics

Read-onlyIdempotent

Race, ethnicity, and age breakdown for a US geography. Returns counts for white, black, Asian, AIAN, NHPI, other, two-or-more, plus Hispanic/Latino total and median age. Source: ACS 5-year.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
msaNo5-digit Metropolitan Statistical Area code. Required for msa level.
yearNoACS 5-year endpoint year (default 2023).
zctaNo5-digit ZIP Code Tabulation Area. Required for zcta level.
levelYesGeography level: 'us', 'state', 'county', 'zcta' (ZIP), 'place' (city), 'tract', 'msa'.
placeNoCensus place FIPS (city). Required for place level.
stateNo2-letter state code (e.g. 'TX') or 2-digit FIPS. Required for state/county/place/tract levels.
tractNo6-digit census tract code. Use '*' for all tracts in a county.
countyNo3-digit county FIPS. Use '*' for all counties in a state. Required for county/tract levels.

TDQS

A4/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive, and open-world behavior. The description adds value beyond those by specifying the source (ACS 5-year) and enumerating the returned fields, which helps an agent understand what data to expect without an output schema.

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 three tight sentences, front-loading the core purpose and immediately listing the returned fields and data source. Every sentence carries useful information with no redundancy.

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?

Given the well-documented schema and safety annotations, the description is largely complete: it names the geography scope, source, and expected return fields. Since there is no output schema, a bit more detail about edge cases or estimates would improve it, but nothing critical is missing.

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 coverage is 100%, so the parameters are already fully documented. The description adds only implicit context—US geography and ACS 5-year—rather than new parameter-level meaning, which matches the baseline for fully covered schemas.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns race, ethnicity, and age breakdowns for a US geography, naming the specific output categories. It is distinct from siblings like census_population and census_income_housing by content, but does not explicitly differentiate itself from them.

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 makes the use case clear: use this tool when you need demographic breakdowns (race, ethnicity, age) for a US geography. It gives no explicit exclusions or alternative tool recommendations, but the intended context is apparent.

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