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LiveDataLink

by io.github.blackboxfoundry

geocode_address

Read-onlyIdempotent

Geocode a US street address with the Census Geocoder (keyless). Accepts a one-line address OR structured street/city/state/zip parts. Returns the normalized matched address, longitude/latitude, and Census geographies (state, county, tract, block, congressional district) with GEOIDs. No match returns an explicit not-found message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zipNoZIP code for a structured query.
cityNoCity for a structured query.
stateNoTwo-letter state code for a structured query (e.g. 'DC').
streetNoStreet line for a structured query (e.g. '4600 Silver Hill Rd').
addressNoOne-line address, e.g. '4600 Silver Hill Rd, Washington, DC 20233'. Provide this OR the structured parts.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already mark this as readOnly, idempotent, and non-destructive; the description adds what the caller receives: normalized matched address, coordinates, Census geographies with GEOIDs, and an explicit not-found message on failure. This meaningfully extends beyond the annotations without contradicting them.

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 sentences, each carrying new information: operation and source, accepted input forms, and return/failure behavior. There is no filler and no repetition of schema details.

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 single-address geocoder with no output schema, the description is largely complete: it names the service, scope, input alternatives, output fields, and failure behavior. A minor gap is that it doesn't state that at least one of the two input modes must be supplied despite zero required parameters.

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 input schema has 100% description coverage, so each parameter is already documented. The description adds the critical relationship that `address` is an alternative to the `street`/`city`/`state`/`zip` group, helping an agent choose a valid call shape.

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 opens with a specific verb and resource ('Geocode a US street address') and identifies the exact service ('Census Geocoder (keyless)'). It distinguishes the tool from reverse-geocoding or batch siblings by focusing on forward address geocoding with a clear return payload. This is unambiguous and differentiated.

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?

Clear context is given: use for a single US street address, supplied either as a one-line address or as structured street/city/state/zip parts. It does not explicitly name alternatives like geocode_batch or geocode_coordinates, so no explicit when-not guidance is provided, but the intended use is evident.

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