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mcp-drizly

mcp-drizly

by markswendsen-code
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

get_stores

Find local alcohol retailers that deliver to your address. Enter your delivery location to see available stores for ordering drinks.

Instructions

Get available stores and retailers that can deliver to a specific address

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": {
      "address": "<address>"
    },
    "name": "get_stores"
  }
}

Input Schema

NameRequiredDescriptionDefault
addressYesDelivery address to find available stores for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states the tool's purpose but lacks behavioral details such as whether it requires authentication, has rate limits, returns paginated results, or what the output format looks like (e.g., list of stores with delivery options). For a tool with no annotation coverage, this is a significant gap.

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 a single, efficient sentence that front-loads the core purpose ('Get available stores and retailers') and adds necessary scope ('that can deliver to a specific address'). There is zero waste or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose but lacks behavioral context (e.g., response format, error handling) and usage guidelines. Without annotations or output schema, more detail would improve completeness for an AI agent.

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 schema already documents the single 'address' parameter. The description adds marginal value by implying the address is used for delivery eligibility, but doesn't provide additional context like address format requirements or examples. Baseline 3 is appropriate when the schema does the heavy lifting.

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 specific action ('Get available stores and retailers') and the resource ('stores and retailers'), with explicit scope ('that can deliver to a specific address'). It distinguishes from siblings like 'search_products' or 'browse_categories' by focusing on delivery-eligible stores rather than products or categories.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. While the description implies it's for finding stores that deliver to an address, it doesn't specify prerequisites (e.g., whether the address must be validated first), exclusions, or comparisons to siblings like 'check_availability' (which might overlap in functionality).

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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