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infobel-api-mcp

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by techinfobel

search_categories_alt_international

Find NACE codes (European industry classification) by entering one or more keywords. Use these codes to filter business searches by EU industry categories.

Instructions

Search NACE codes (European standard, AltInternational) by one or more keywords.

Each keyword triggers a separate API call; results are merged and deduplicated. Returns matching NACE codes for use in search_businesses alt_international_codes field. Use this for EU industry classification queries.

Args: keywords: One or more search terms (e.g. ["computer programming"], ["software", "IT", "development"]). language_code: Display language for results (e.g. "en", "fr", "de").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsYes
language_codeNoen

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that each keyword triggers a separate API call and results are merged/deduplicated, which is critical behavioral information. It does not mention side effects or rate limits, but the given detail adds significant value beyond the schema.

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 concise, with the first sentence stating the purpose, followed by behavioral details and usage context. The Args section is structured clearly. No unnecessary words exist.

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?

Given the presence of an output schema and the tool's search functionality, the description covers purpose, behavior (multi-call, merge), usage domain, and parameter details. It sufficiently informs an AI agent without missing critical aspects.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It provides detailed parameter descriptions with examples for both keywords and language_code, including default values and usage context, fully covering the meaning of each parameter.

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 searches NACE codes (European standard) by keywords, differentiates from sibling tools like search_categories_infobel and search_categories_local by specifying EU industry classification, and mentions the specific classification system (AltInternational). This provides a specific verb and resource with clear distinction.

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 explicitly states 'Use this for EU industry classification queries' and notes the results are intended for the search_businesses tool's field, providing clear usage context. However, it does not explicitly exclude alternatives or mention when not to use it, so a slight deduction for lacking exclusions.

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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