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discava – Business Directory for AI

search_businesses

Search for local businesses worldwide using location, category, or keywords to find names, categories, and quality scores for AI-driven analysis.

Instructions

Search for local businesses. Returns name, category, city, country, logo_url, available_details (what data exists), and scores. Check available_details to see what is available, then call get_business for full details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoSearch query (e.g. "plumber", "Zahnarzt", "Italian restaurant")
cityNoCity name (e.g. "Hamburg", "Wien", "New York")
countryYesISO country code (e.g. "DE", "AT", "CH", "US"). Required.
categoryNoCategory slug (e.g. "plumber", "restaurant", "dentist")
limitNoNumber of results (1-50, default 10)
pageNoPage number
min_confidenceNoMin confidence score 0-100
langNoLanguage for labels: "de" or "en"en
latNoLatitude for distance
lonNoLongitude for distance

Implementation Reference

  • server.ts:53-81 (handler)
    The 'search_businesses' tool is registered and implemented directly in server.ts using the McpServer.tool method. It takes various query parameters, constructs a URL, calls the discava API, and returns the result.
    server.tool(
      'search_businesses',
      'Search for local businesses. Returns name, category, city, country, logo_url, available_details (what data exists), and scores. Check available_details to see what is available, then call get_business for full details.',
      {
        query: z.string().optional().describe('Search query (e.g. "plumber", "Zahnarzt", "Italian restaurant")'),
        city: z.string().optional().describe('City name (e.g. "Hamburg", "Wien", "New York")'),
        country: z.string().describe('ISO country code (e.g. "DE", "AT", "CH", "US"). Required.'),
        category: z.string().optional().describe('Category slug (e.g. "plumber", "restaurant", "dentist")'),
        limit: z.number().optional().default(10).describe('Number of results (1-50, default 10)'),
        page: z.number().optional().default(1).describe('Page number'),
        min_confidence: z.number().optional().describe('Min confidence score 0-100'),
        lang: z.string().optional().default('en').describe('Language for labels: "de" or "en"'),
        lat: z.number().optional().describe('Latitude for distance'),
        lon: z.number().optional().describe('Longitude for distance'),
      },
      async ({ query, city, country, category, limit, page, min_confidence, lang, lat, lon }) => {
        const params = new URLSearchParams({ country });
        if (query) params.set('q', query);
        if (city) params.set('city', city);
        if (category) params.set('category', category);
        if (limit) params.set('limit', String(limit));
        if (page) params.set('page', String(page));
        if (min_confidence) params.set('min_confidence', String(min_confidence));
        if (lang) params.set('lang', lang);
        if (lat !== undefined) params.set('lat', String(lat));
        if (lon !== undefined) params.set('lon', String(lon));
        return jsonContent(await api(`/search?${params}`));
      }
    );
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return structure and the workflow with get_business, which adds value. However, it doesn't mention rate limits, authentication requirements, pagination behavior beyond the page parameter, error conditions, or whether this is a read-only operation. For a search tool with 10 parameters and no annotations, more behavioral context would be helpful.

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 extremely concise and well-structured in just two sentences. The first sentence states the purpose and return fields, while the second provides crucial workflow guidance. Every word earns its place with zero wasted text, making it front-loaded and efficient.

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?

Given the complexity (10 parameters, no annotations, no output schema), the description does a good job of explaining the core purpose and workflow. It clarifies the relationship with get_business, which is important context. However, for a search tool with many parameters and no output schema, more information about result format, pagination behavior, or error handling would make it more complete.

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 all 10 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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 tool's purpose with specific verb ('Search') and resource ('local businesses'), and distinguishes it from sibling get_business by explaining the relationship between search results and detailed data retrieval. It explicitly mentions the return fields (name, category, city, etc.), making the purpose highly specific and differentiated.

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?

The description provides explicit guidance on when to use this tool versus alternatives: 'Check available_details to see what is available, then call get_business for full details.' This clearly establishes the workflow relationship with the sibling get_business tool, indicating this is for initial search and the other for detailed retrieval.

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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