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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already cover read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context beyond annotations: it is keyless, returns the normalized matched address, coordinates, Census geographies with GEOIDs, and explicitly reports an unmatched address with a not-found message.

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 tightly written sentences front-load the core purpose, then cover input modes and outputs. Every sentence adds necessary selection or invocation information with no repetition or filler.

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?

With no output schema, the description compensates by enumerating the key return components including normalized address, coordinates, Census geographies, GEOIDs, and not-found behavior. For a five-parameter tool with zero required fields and no output schema, this is sufficient for correct invocation.

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 already has 100% parameter coverage with clear examples. The description adds useful relational semantics by stating that callers can pass either a one-line address OR structured street/city/state/zip parts, clarifying the intended parameter grouping beyond the schema's individual descriptions.

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 with the Census Geocoder (keyless).' It clearly distinguishes this tool from sibling tools like geocode_coordinates and geocode_batch by scope (street address) and single-address mode.

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 gives clear context: US-only addresses, keyless Census Geocoder, and two accepted input modes (one-line vs. structured parts). It does not explicitly name alternatives or state when not to use the tool, but the 'street address' framing implies the boundary versus coordinate/batch geocoding.

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

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.

Resources