ares-mcp

ares_get_res_classification

Retrieve statistical classification for Czech companies from RES: size (SME/large), institutional sector, CZ-NACE code, financial office, and NUTS region for B2B segmentation and reporting.

Instructions

Get statistical classification of a Czech company from RES (Registr ekonomických subjektů): headcount bracket (decoded into SME / large), institutional sector (ESA 2010), primary CZ-NACE, financial office (FÚ), and NUTS region. Useful for B2B segmentation, reporting and policy-style analysis.

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": {
      "ico": "<ico>"
    },
    "name": "ares_get_res_classification"
  }
}

Input Schema

Name	Required	Description	Default
`ico`	Yes	Czech IČO of the company.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.2Aug 7, 2026

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. The verb 'Get' implies a read operation, and the output fields are listed, but there is no explicit statement about read-only behavior, error handling, rate limits, or authorization requirements. It is adequate but not thorough.

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 two sentences, front-loaded with the action and resource, then details the output fields and use cases. Every sentence adds value with no redundancy or filler.

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?

With one parameter and no output schema, the description covers the return fields and intended use cases, providing a good overall picture. However, it does not mention edge cases like invalid ICO or missing data, which would complete the picture.

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?

The schema already covers the single parameter 'ico' with a description ('Czech IČO of the company'), achieving 100% coverage. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline but does not exceed it.

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 'gets statistical classification of a Czech company from RES' and enumerates specific data points (headcount, sector, CZ-NACE, FÚ, NUTS), distinguishing it from sibling tools like ares_get_statutory_bodies or ares_lookup_cz_nace.

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?

It provides clear use contexts ('B2B segmentation, reporting and policy-style analysis') but does not explicitly name alternatives or exclusionary criteria. This gives context without explicit when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server