CryptoPulse Data API

halal_near

[$0.005 USDC per call via x402] Halal restaurants, grocers, butchers and caterers near a lat/lng (radius up to 30 km), sorted by distance, with cuisine and type filters. OpenStreetMap data (ODbL).

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": {
      "lat": "<lat>",
      "lng": "<lng>"
    },
    "name": "halal_near"
  }
}

Input Schema

Name	Required	Description
`lat`	Yes	Latitude, e.g. 40.7128
`lng`	Yes	Longitude, e.g. -74.0060
`type`	No	Filter by place type: restaurant, fast_food, cafe, butcher, supermarket, convenience
`limit`	No	Max results, 1-50 (default 20)
`cuisine`	No	Filter by cuisine substring, e.g. indian, turkish, pakistani
`radius_km`	No	Search radius in km, 0.2-30 (default 5)

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 3, 2026

TDQS

A3.7/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does disclose meaningful behavior: it is a paid call ($0.005 USDC via x402), the underlying source and license (OpenStreetMap, ODbL), and that results are distance-sorted with a 30 km ceiling. It omits return shape, empty-result behavior, and any rate/error semantics, which keeps it short of full marks.

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?

A single dense sentence covering scope, filters, sorting, and data source, plus a bracketed cost tag. Every clause carries information, though leading with the price tag rather than the purpose slightly delays what the tool does.

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 lookup with no output schema and fully documented parameters, the description covers cost, source, ordering, and geographic limits. The main residual gap is what a result record looks like, which matters given no output schema is supplied.

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 parameters are already documented, and the description only restates the radius ceiling and the existence of cuisine/type filters. It adds no format hints or interaction notes beyond the schema, so the baseline of 3 applies.

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?

States a specific verb-equivalent ('near a lat/lng') and a concrete resource set (halal restaurants, grocers, butchers, caterers), plus sorting and filter behavior. It does not differentiate itself from siblings like halal_city or halal_place, so an agent must infer the distinction from names alone.

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?

The scope ('near a lat/lng, radius up to 30 km') implies when this tool applies, and the radius cap hints at geographic limits. However, it never names halal_city or halal_place as alternatives or states when those should be preferred, leaving the routing decision implicit.

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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