Crawlora MCP

grubhub_search

Read-only

Search Grubhub restaurants delivering to or offering pickup at latitude/longitude, optionally filtering names, cuisines, or dishes. Results include restaurant IDs, delivery and pickup fees, ETAs, cuisines, ratings, and menu counts; unsupported locations return an empty list. Use grubhub_restaurant for one store or grubhub_restaurant_menu for priced items.

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": {
      "latitude": "<latitude>",
      "longitude": "<longitude>"
    },
    "name": "grubhub_search"
  }
}

Input Schema

Name	Required	Description
`page`	No	Optional. 1-based page number, default 1.
`search`	No	Optional. Keyword over restaurant names, cuisines and dishes.
`latitude`	Yes	Required. Delivery search center latitude.
`longitude`	Yes	Required. Delivery search center longitude.
`page_size`	No	Optional. Restaurants per page, 1-50, default 20.
`order_method`	No	Optional. delivery or pickup. Default delivery.

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 5, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds genuine behavioral value beyond that: it lists the returned fields (IDs, fees, ETAs, cuisines, ratings, menu counts) and discloses the edge case that unsupported locations return an empty list rather than an error. It stops short of describing pagination behavior, but this is well above the annotation baseline.

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?

Three sentences, front-loaded with the core action and scope before the return details and routing advice. Every sentence carries information, though the results-field enumeration is somewhat dense and could be trimmed.

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?

For a read-only search tool with an output schema already declaring the return shape, the description covers action, scope, filter options, output summary, edge case, and alternatives. Not much an agent needs is missing; only pagination semantics and the interaction between page and page_size are left implicit.

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 all six parameters (page, search, latitude, longitude, page_size, order_method) are already documented in the schema. The description reinforces the lat/long center and keyword filtering but adds no format, range, or default detail beyond what the schema states. Baseline 3 applies.

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 (search) plus resource (Grubhub restaurants) and pins the scope to delivery/pickup at a latitude/longitude, with optional name/cuisine/dish filtering. An agent can immediately distinguish it from grubhub_restaurant (single store) and grubhub_restaurant_menu (priced items) without inspecting schemas.

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

Usage Guidelines5/5

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

Explicitly routes to alternatives: 'Use grubhub_restaurant for one store or grubhub_restaurant_menu for priced items.' It also sets the context (delivery vs pickup, location-based) that selects this tool over the detail endpoints, leaving little to inference.

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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