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groundroof

CREHQ MCP Server

by groundroof

crehq_locations_nearby

Search for tracked storefronts by radius from any latitude/longitude point, using distance-sorted, government-verified data for trade-area analysis and competitor mapping.

Instructions

Radius search: find all tracked locations within N miles of a lat/lng point. Powers trade-area analysis, competitor mapping, and 'what's near this address' questions. Returns distance-sorted, government-verified storefronts across every vertical CREHQ covers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude (decimal degrees).
lngYesLongitude (decimal degrees).
brandNoOptional: restrict to one brand.
categoryNoOptional: restrict to one vertical/category.
per_pageNoResults per page (max 200, default 50).
radius_miNoSearch radius in miles (default 5).
include_provenanceNoFor CREHQ Pro self-serve keys, include D2 provenance, source, confidence and first-observed fields. Free sandbox keys will return upgrade intent.
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that results are distance-sorted and government-verified, which adds behavioral context. However, it does not specify authentication requirements, rate limits, or potential side effects (though likely read-only). This is adequate but not rich.

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?

The description is concise, front-loaded with the core purpose ('Radius search'), and every sentence adds value. It fits in two sentences with no redundant or vague wording.

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?

The description, combined with a fully documented schema, covers the tool's functionality well. It explains the return format (distance-sorted, government-verified storefronts) and mentions coverage across verticals. While there is no output schema, the description provides sufficient context for an agent to invoke the tool correctly. Slightly less complete than the TDQS 4.3 reference due to more parameters, but still strong.

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 parameters are already well-documented in the schema. The description adds use-case context but does not provide additional semantics for individual parameters beyond what the schema offers. This aligns with the baseline score of 3.

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 clearly states the tool's function as a radius search for locations near a lat/lng point. It uses a specific verb ('find') and resource ('tracked locations') within a defined scope (radius in miles), and it is distinguishable from sibling tools like locations_search and locations_list.

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 explicitly lists use cases ('trade-area analysis, competitor mapping, and what's near this address questions'), providing clear context for when to use the tool. It does not explicitly name alternatives or give exclusions, but the use cases are sufficient guidance.

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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