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mcp-einvoicing-be

mcp-einvoicing-be

by cmendezs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

resolve_peppol_dns

Resolve the SMP hostname for a Peppol participant using DNS U-NAPTR, verifying SML registration without contacting the SMP.

Instructions

Resolve the SMP hostname for a Peppol participant via DNS only.

Performs the raw U-NAPTR (SML) lookup without fetching the SMP service group, useful for diagnosing whether a participant is registered in the SML independently of SMP reachability.

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": {
      "identifier": "<identifier>"
    },
    "name": "resolve_peppol_dns"
  }
}

Input Schema

NameRequiredDescriptionDefault
identifierYesPeppol participant ID or adaptable national identifier.
environmentNo"production" or "test".production

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.9.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral burden and does well: it discloses that the tool is DNS-only, performs a raw U-NAPTR lookup, and explicitly does not fetch the SMP service group. This gives the agent an accurate model of side effects and limitations, though it does not discuss network dependency or error behavior.

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 tightly scoped sentences: the first states the core action, the second adds the key behavioral distinction and use case. No filler or repetition exists.

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?

For a simple two-parameter tool with an output schema, the description is largely complete: it explains the operation, its scope, and its diagnostic purpose. It could slightly improve by naming a sibling alternative, but this is not required for correctness.

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 both parameters well. The description reinforces what 'identifier' refers to implicitly via 'participant', but does not add new parameter-level detail beyond the schema. Baseline 3 is appropriate.

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 names a specific verb ('Resolve'), resource ('SMP hostname for a Peppol participant'), and method ('via DNS only'). It further distinguishes itself from sibling tools by clarifying it performs the raw U-NAPTR lookup without fetching the SMP service group.

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 clearly implies when to use it: for diagnosing SML registration independently of SMP reachability. It does not explicitly name alternative tools or when not to use it, but the context is strong enough that an agent can infer the appropriate scenario.

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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