ares-mcp

ares_export_for_invoicing

Transform a Czech company's ARES profile into a ready-to-use payload for Fakturoid, iDoklad, or Pohoda, filling identification and address fields for invoicing.

Instructions

Transform an ARES company profile into a payload ready for a Czech invoicing system: Fakturoid (JSON), iDoklad (JSON), or Pohoda (XML-hint JSON). Pure data transformation — no calls to the target system. The result is a paste-ready or post-ready object that fills the identification + address fields; the caller adds contact, bank and pricing.

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>",
      "target": "<target>"
    },
    "name": "ares_export_for_invoicing"
  }
}

Input Schema

Name	Required	Description	Default
`ico`	Yes	Czech IČO of the entity to export.
`target`	Yes	Invoice-system target format. `fakturoid` returns a JSON shape ready to POST to https://app.fakturoid.cz/api/v3/{slug}/subjects.json. `idoklad` returns a JSON shape compatible with https://api.idoklad.cz Contact endpoint. `pohoda` returns an XML-hint JSON that maps onto Pohoda's `<dat:address>` element in its mServer / XML export schemas.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.2Aug 7, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that the tool performs a pure data transformation, makes no external calls, and returns a paste-ready/post-ready object with only identification and address fields. This is valuable behavioral context that goes beyond the tool name, though it does not discuss error handling or rate limits.

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 three sentences, front-loaded with the core purpose, and every sentence adds unique information: the target formats, the no-calls caveat, and the output scope. There is zero waste or repetition.

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 tool has only 2 parameters and no output schema, the description adequately explains the output shape and field coverage. It names the target-specific formats and the caller's responsibility. It could mention error cases (e.g., ICO not found) but is sufficient for a simple transformation tool with clear schema descriptions.

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 baseline is 3. The description reinforces the target format and the overall purpose but adds no extra per-parameter syntax or constraints beyond what the schema already states. The schema's target enum descriptions are already detailed, and the description does not need to repeat them.

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 uses a specific verb ('Transform') and resource ('ARES company profile into a payload'), names three concrete target systems (Fakturoid, iDoklad, Pohoda), and clearly distinguishes this from all sibling ARES search/validation tools. It leaves no ambiguity about what the tool does.

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 gives clear context: it is a pure data transformation with no calls to the target system, and the output fills identification + address fields while the caller supplies the rest. It does not explicitly name alternatives or say 'use when X, not when Y', but the unique purpose makes that unnecessary.

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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