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Crawlora MCP

Crawlora MCP

Official
by Crawlora-org
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

culvers_directory

Browse a paginated index of Culver's restaurant URLs and slugs, covering about 1,121 locations. Use page and page_size parameters to navigate results.

Instructions

Browse Culver's restaurant-URL index. Returns one page of Culver's restaurant index, which covers about 1,121 restaurants. page starts at 1 and page_size runs 1 to 500 (default 100). Each entry has a slug, the value the /culvers/store endpoint takes, and the restaurant's URL. The total and page counts are included.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "culvers_directory"
  }
}

Input Schema

NameRequiredDescriptionDefault
pageNoOptional. 1-based page, default 1.
page_sizeNoOptional. Entries per page, 1-500, default 100.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral burden, and it does so reasonably: it discloses pagination (1-based pages, size 1-500, default 100), the total corpus size, the per-entry fields (slug and URL), and that total/page counts are returned. It does not explicitly state read-only/no-side-effects or ordering, keeping it short of a 5.

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?

Four compact sentences, correctly front-loaded with purpose before mechanics. Minor redundancy between 'restaurant-URL index' and 'restaurant index', but nothing is wasted and the sequence is logical.

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?

With no output schema and no annotations, the description compensates by describing return entry shape (slug, URL), pagination fields, and totals. It leaves ordering and exact response envelope unspecified, but otherwise an agent has enough to call it correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the parameter definitions already exist; the description mostly restates the same ranges. It adds genuine value with the '~1,121 restaurants' total, which gives an agent the context needed to reason about how many pages to request, going slightly beyond the schema.

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?

States a specific verb and resource ('Browse Culver's restaurant-URL index') and defines the scope precisely (~1,121 restaurants, one page at a time). It also explains how output relates to a sibling endpoint ('the value the /culvers/store endpoint takes'), which lets an agent distinguish this enumeration tool from a store-detail tool without opening schemas.

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?

Usage is implied: this is the tool to enumerate the full restaurant list so you can obtain slugs. There is no explicit when-to-use/when-not or a named alternative (e.g., 'use culvers_store for one restaurant's details'), so an agent must infer routing from context.

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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