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Glama

OpenDealer MCP Server

Find Nearby Dealers

dealers_near
Read-onlyIdempotent

Find dealerships near a location.

Location modes (choose ONE): • zip + radius (miles) • lat + lng + radius • city + state + radius • county + state + radius

Returns dealer information including: • Name, address, phone, website • Distance from search location • Current inventory count • Business hours (when available)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude coordinate
lngNoLongitude coordinate
zipNo5-digit US ZIP code (e.g., 92688)
cityNoCity name
limitNoMaximum number of dealers to return (1-200, default: 50)
stateNo2-letter US state code (e.g., CA, TX)
countyNoCounty name
offsetNoNumber of results to skip for pagination
radiusNoSearch radius in miles (1-250, default: 50)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsNo
totalNo
dealersNo

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds value by detailing the return information (name, address, inventory count, hours), but does not disclose additional behavioral traits beyond what annotations 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?

The description is concise and well-structured, with the main purpose front-loaded. Every sentence adds value, specifying location modes and return fields without extraneous information.

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?

Given the 9 parameters, existing output schema, and rich annotations, the description fully covers all aspects: location modes, parameter groupings, and return fields. No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with parameter descriptions, but the description adds critical grouping and exclusivity information (e.g., 'choose ONE' location mode) that enhances understanding beyond the schema alone.

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 verb 'Find' and resource 'dealerships near a location,' with specific location modes and return fields. It effectively distinguishes from sibling tools like 'get_dealer' and 'dealer_inventory'.

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 states location modes and the requirement to 'choose ONE,' providing clear context for use. However, it does not explicitly mention when not to use this tool or compare to alternatives, though sibling tool names offer some differentiation.

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

A4.2/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose. Tools like compare_market, compare_models, and compare_vehicles target different scopes (segment, model, VIN), while search_vehicles and filter_vehicles serve different query types. No apparent overlap.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., check_recalls, compare_market, get_vehicle, list_facets). No mixing of naming conventions, making the set predictable and easy to navigate.

Tool Count4/5

With 26 tools, the count is on the higher side but appropriate for a comprehensive automotive platform. Each tool covers a distinct aspect of car shopping and research, and there is no obvious redundancy.

Completeness5/5

The tool surface covers the full lifecycle of automotive research and shopping: recalls, comparisons, dealerships, vehicle search and details, history, deal scoring, market trends, safety ratings, rankings, and lead submission. Only minor gaps like trade-in valuation exist.

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