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groundroof

CREHQ MCP Server

by groundroof

crehq_co_tenancy

Identify which brands co-locate near a given brand's stores to support site selection, anchor-tenant matching, and trade-area benchmarking.

Instructions

PREMIUM INTELLIGENCE — co-tenancy analysis: which brands most often co-locate within a given radius of this brand's stores (the chains that cluster together: e.g. who anchors near Chipotle). Drives site-selection, anchor-tenant matching, and trade-area benchmarking. (Intel & Enterprise tiers.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_idYesCREHQ company id to analyze.
radius_metersNoCo-location radius in meters (default 200).
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the premium tier restriction and implies the output is a ranked list of co-locating brands, but lacks details on pagination, result limits, data freshness, or rate limits. This is adequate but not comprehensive.

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 compact and well-structured. Each sentence earns its place: the premium qualifier, the core explanation with an example, and the practical use cases. No redundant or unnecessary text.

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?

Given the simple schema and the absence of an output schema, the description covers the core purpose and use cases well. It conveys the output as co-locating brands, though it doesn't specify result limits or ranking details, which are minor omissions for tool selection.

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?

The input schema already provides complete descriptions for both parameters (company_id and radius_meters), so the tool description adds no extra parameter semantics. Schema coverage is 100%, justifying 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 defines the tool's purpose: co-tenancy analysis identifying which brands co-locate near a given brand's stores. The example using Chipotle clarifies the concept, and the description distinguishes this tool from siblings by focusing on cross-brand clustering patterns rather than individual locations or contacts.

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 provides explicit use cases (site-selection, anchor-tenant matching, trade-area benchmarking) which imply when to use this tool. However, it does not explicitly name alternative tools or state when not to use it, so it falls 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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