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Datafiniti

by io.github.pipeworx-io
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Datafiniti Business Search

datafiniti_business_search
Read-onlyIdempotent

Search Datafiniti's business/location database — returns name, address, category, phone, website, lat/lon, and reviews. Uses the Datafiniti field:value query language. Example: datafiniti_business_search({ query: "country:US AND categories:Restaurant AND city:"Seattle"", num_records: 5, _apiKey: "your-token" })

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "country:US AND city:\"Seattle\" AND categories:Restaurant",
      "_apiKey": "your-datafiniti-api-key",
      "num_records": 10
    },
    "name": "datafiniti_business_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesDatafiniti query, e.g. `country:US AND city:"Seattle" AND categories:Restaurant`. Fields: name, categories, city, province, postalCode, country, phones, websites.
_apiKeyYesDatafiniti API token (from https://portal.datafiniti.co/). Sent as Authorization: Bearer.
num_recordsNoMax records to return (default 10, set 0 to get only num_found at no credit cost).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful context by enumerating returned fields and stating that queries must use Datafiniti's field:value query language, with a concrete example. No contradiction with annotations.

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 compact and front-loaded: purpose first, then the query-language constraint, then a practical example. There is no fluff or redundant restatement of the schema.

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?

With annotations covering safety semantics and the schema covering parameters, the description compensates for the missing output schema by naming the actual returned fields. The example plus schema gives an agent everything needed to invoke the tool 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?

The schema already documents all three parameters with 100% coverage, but the description adds a complete invocation example and explicitly identifies the field:value query syntax. This gives an agent additional guidance for constructing valid queries beyond the schema text.

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 states a specific action on a specific resource: Search Datafiniti's business/location database. It also lists the returned fields, making the tool's domain clear and distinguishable from sibling tools like datafiniti_product_search and datafiniti_property_search.

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 makes it clear when to use this tool: when business/location data from Datafiniti is needed. It does not explicitly name alternatives or exclusion conditions, but the business/location scope is clear enough to guide selection.

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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