Skip to main content
Glama

LiveDataLink

by io.github.blackboxfoundry

census_business

Read-onlyIdempotent

Business establishments, employment, and annual payroll from County Business Patterns. Optional NAICS industry filter. Used for industry research, competitive intel, supply chain analysis.

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'.
naicsNoOptional NAICS 2017 industry code (2 to 6 digits). E.g. '23' for Construction, '54' for Professional Services.
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 indicate read-only, idempotent, and non-destructive behavior. The description adds useful context beyond that by naming the exact data covered (establishments, employment, payroll), the source (County Business Patterns), and the optional NAICS filter, which helps set expectations for the response.

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?

Three concise sentences with the core data content and source front-loaded. The use cases at the end are useful context, and there is no redundant or filler language.

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 tool with 9 parameters and no output schema, the description is somewhat high-level, but the schema covers parameter semantics and required/conditional fields. The description conveys what data is returned, the optional industry filter, and when to use it, which is enough for an agent to select and safely 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%, so the schema already documents all parameters, giving a baseline of 3. The description briefly reinforces the optional NAICS filter but does not add meaningful syntax, format, or cross-parameter context beyond what the schema provides.

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 states a clear data resource: business establishments, employment, and annual payroll from County Business Patterns, so an agent can tell it pulls business/economic data. However, it does not explicitly distinguish it from sibling census tools like census_demographics, census_population, or census_commute_employment, so it stops short of full differentiation.

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 final sentence gives concrete intended uses ('industry research, competitive intel, supply chain analysis'), providing clear context for when to select this tool. It does not name alternatives or exclusions, but the use-case framing is sufficiently clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All LiveDataLink toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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