CREHQ MCP Server

crehq_recent_location_context

Get recent location context: timing, address, traffic counts, demographics, coverage. Answer queries about observed locations, distinguishing verified openings from first-observed events.

Instructions

Context for a brand's most recently observed locations: event timing, address/market, traffic counts when backfilled, route class, trade-area demographics, radius demographics, drive-time context, and coverage flags. Useful for questions like 'traffic counts for the last 50 Starbucks locations CREHQ observed.' Event rows distinguish verified openings from first-observed/reconciliation events.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "company_id": "<company_id>"
    },
    "name": "crehq_recent_location_context"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Max locations to return (default 50, max 500).
`country`	No	ISO country code filter (default all available rows).
`company_id`	Yes	CREHQ company id.
`event_type`	No	Lifecycle event type to use for recency (default first_observed).
`only_with_traffic`	No	When true, return only recent rows with traffic/AADT attached.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.7Aug 7, 2026

TDQS

A3.5/5.0

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 that traffic counts are only present 'when backfilled' and that event rows distinguish verified openings from other events. However, it does not describe other behavioral traits such as read-only nature, sorting order, or response structure beyond the content list.

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?

The description is two sentences, front-loaded with the core purpose and key content categories, and includes a concrete example. It is concise without being overly terse, though a direct verb at the start would be slightly more effective.

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 tool's moderate complexity and lack of output schema, the description covers the main content areas (event timing, traffic, demographics, etc.), provides a usage example, and clarifies event row semantics. The schema supplies parameter defaults, so the description is sufficiently complete for selection and basic invocation.

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 the baseline is 3. The description does not add parameter-level details beyond the schema (e.g., defaults for limit, event_type, or the meaning of only_with_traffic), so it neither improves nor detracts.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides 'context for a brand's most recently observed locations' and lists the types of context included. It differentiates from sibling tools by focusing on recency and providing a concrete example question, though it lacks an explicit action verb like 'retrieve' or 'get'.

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

Usage Guidelines3/5

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

Includes a helpful example question ('traffic counts for the last 50 Starbucks locations CREHQ observed') that implies appropriate use cases, but does not explicitly state when to use this tool over alternatives like crehq_location_history or crehq_locations_list, nor does it mention any exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.