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acuris-mcp

by com.acuris-geo

geocode_address

Forward-geocode an address to coordinates (lat/lng) with an explicit accuracy_type — you always know what you got.

accuracy_type is an OPEN set; new tiers are added as reference data
improves. Do NOT write code that treats an unrecognised value as a failure.
Branch on the boolean `point_coordinate` instead, which is true when the
coordinate identifies one address. Current values: rooftop and
arrival_point (both point coordinates — a point on the building, or the
property's entrance / access point), then street_interpolated,
street_centroid, postcode_centroid, locality_centroid, admin_centroid.

House-number-level matches may ALSO carry `arrival_point: {lat, lng}` —
the premise's entrance/access coordinate beside the served pin (equal to
lat/lng when accuracy_type is arrival_point). Use it for delivery and
navigation; omitted when not on file.

`country` is ISO-3 lowercase. Give fielded parts or a single line in `q`.
For UK (gbr) addresses use validate_address instead — it returns point
coordinates from Royal Mail PAF.

Cost: 1 credit per successful geocode; failures are refunded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNo
cityNo
stateNo
streetNo
countryYes
postcodeNo
house_numberNo

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, description carries full burden. It discloses open accuracy_type set, instructs to branch on point_coordinate, explains arrival_point semantics, and states cost/refund behavior. This is rich behavioral context beyond what annotations would typically provide.

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?

Well-structured, front-loaded with the core purpose, then flows logically into accuracy_type, input guidance, UK alternative, and cost. Every sentence adds meaningful detail; no filler or redundant schema repetition.

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?

Despite no output schema, the description explains key response concepts (accuracy_type values, point_coordinate boolean, arrival_point). It covers cost, input methods, country format, and sibling-tool avoidance. For a geocoding tool with no annotations, this is highly complete.

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 has 7 params with zero descriptions, so the text must compensate. It explains country as ISO-3 lowercase and clarifies q vs fielded parts. It also describes arrival_point return object and house-number semantics. Does not individually describe city/state/street/postcode, but they are self-explanatory.

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?

Description opens with a specific verb+resource: 'Forward-geocode an address to coordinates (lat/lng)'. It also notes the explicit accuracy_type, clearly distinguishing from reverse_geocode and UK-specific validate_address siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when not to use: 'For UK (gbr) addresses use validate_address instead'. Also gives input formatting alternatives ('fielded parts or a single line in q') and mentions the credit cost, which helps budget-sensitive agents decide when to call.

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

A4.4/5.0
Disambiguation4/5

Most tools target distinct operations: validation (single/batch), geocoding, reverse geocoding, enrichment, UK postcode expansion, and account management. The main overlap is between enrich_us_ca_address and validate_address, which also includes an enrichment block for US/CA matches, potentially causing confusion about which to use.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., check_balance, expand_uk_postcode, validate_addresses_batch). The naming is uniform and predictable across the set.

Tool Count5/5

8 tools is well within the ideal 3-15 range and appropriately scoped for a geocoding/validation API. Each tool serves a distinct function without unnecessary bloat, and the count reflects the complexity of the domain.

Completeness4/5

The core workflows—single and batch validation, forward and reverse geocoding, enrichment, and UK postcode expansion—are covered. Minor gaps include the lack of a batch geocoding tool and the redundant enrichment functionality already present in validate_address, but these do not create dead ends.

Resources