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Claro AI by Casafari

Location Intelligence

claro_explain_location
Read-only

Location Intelligence for a premises id or any point (lat, lon) in Spain: a footfall index within a 5-minute walk (an index to compare places, not a head count: never say people or visitors a day), split by weekday and weekend, time of day and local, commuter and tourist shares; residents within a 10-minute walk and drive by age band, households by income and spending power; and nearby restaurants, shops, supermarkets, gyms and transit stops. Needs Location Intelligence on the organisation's plan. A figure with no data is null, never 0. Costs 25 tokens a call.

Input Schema

NameRequiredDescriptionDefault
idNoA premises id (pm_...) from a search; returns its stored figures, or measures its point when none are stored. Send id, or lat and lon.
latNoLatitude in degrees of a point in Spain; send with lon instead of id.
lonNoLongitude in degrees of a point in Spain; send with lat instead of id.
langNoLanguage of the text the answer writes (titles, notes, labels, reasons, error messages): en (default) or de (German). Place names, agency names, property titles and quotes from a property's own text stay as written.en

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / lang
      Added value: +{
      +  "default": "en",
      +  "description": "Language of the text the answer writes (titles, notes, labels, reasons, error messages): en (default) or de (German). Place names, agency names, property titles and quotes from a property's own text stay as written.",
      +  "enum": [
      +    "en",
      +    "de"
      +  ],
      +  "type": "string"
      +}
  2. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations cover the safety profile (read-only, non-destructive, closed-world), and the description adds substantial behaviour beyond that: the entitlement requirement, the null-not-zero convention for missing figures, a per-call cost of 25 tokens, and the crucial caveat that the footfall figure is a comparative index, not a head count. That last point prevents a serious misinterpretation an agent would otherwise make.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with what it returns, then constraints and caveats. The long metric enumeration is dense but each item carries information; the definition is not padded, though the opening sentence is heavy for a single sentence.

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 takes on the burden of describing what comes back and does so thoroughly — footfall splits, resident age bands, income bands, and amenity categories — plus cost, entitlement, and null handling. An agent has everything needed to call it and interpret the result correctly.

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% and the schema already explains id vs lat/lon and the lang enum in detail. The description only restates the id-or-point choice without adding format or constraint detail, so it sits at the baseline for a fully documented schema.

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?

States a specific verb+resource: location intelligence metrics (footfall index, resident demographics, nearby amenities) for a premises id or a lat/lon point in Spain. The enumerated metric families make it clearly distinct from siblings like claro_get_premises or claro_compare_premises.

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?

Makes the input choice explicit (a premises id, or a point via lat/lon) and states a hard prerequisite: the organisation's plan must include Location Intelligence. It does not explicitly name sibling alternatives or when to prefer claro_compare_premises for multi-site comparison, which keeps it short of a 5.

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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