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normkompass-mcp

normkompass-mcp

by sotream
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

ask_law

Read-onlyIdempotent

Ask a legal question about German federal law and receive citation-backed answers with verified verbatim quotes, or an explicit refusal when the corpus lacks support.

Instructions

Ask a question and get an answer with citations, or an explicit refusal when the corpus does not support an answer. normkompass checked that every quote in the citations is a verbatim part of a retrieved passage; this server adds nothing to it. Needs an answer model configured in normkompass. German federal law (UStG, UStDV, AO, BDSG, DDG). Not legal advice. Every result carries the Stand (version date) of the law it comes from.

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": {
      "question": "<question>"
    },
    "name": "ask_law"
  }
}

Input Schema

NameRequiredDescriptionDefault
questionYes

Output Schema

NameRequiredDescriptionDefault
standYes
answerYes
messageYes
refusedYes
citationsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

Well beyond the annotations: it discloses that a refusal is a legitimate outcome when the corpus does not support an answer, explains the citation-verification provenance (quotes checked verbatim upstream, server adds nothing), states that every result carries the Stand/version date, and adds a 'not legal advice' caveat. The closed-world, read-only, idempotent profile is already covered by annotations, so this extra behavioral detail is the valuable part.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core outcome and refusal semantics, then prerequisites, scope, and caveats in short declarative sentences. Most sentences earn their place; 'this server adds nothing to it' is slightly cryptic and could be phrased more plainly.

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?

An output schema exists, so return structure need not be explained, yet the description still flags the Stand field and refusal shape, which is genuinely useful. Given annotation coverage and the single-parameter schema, what remains missing (question phrasing guidance, sibling routing) is minor rather than blocking.

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?

Only one parameter with 0% schema description coverage, so the description must carry the load. It does add meaning by constraining the domain of valid questions to German statutory law (UStG, UStDV, AO, BDSG, DDG), but it says nothing about question phrasing, length, or language expectations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Specific verb+resource: ask a question, receive an answer with citations or an explicit refusal over German federal law. An agent can tell it is a question-answering tool rather than a retrieval tool. However, the sibling search_law is never named, so the boundary between 'ask' and 'search' is only implied by the word choice.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a real precondition ('Needs an answer model configured in normkompass') and scopes the corpus (UStG, UStDV, AO, BDSG, DDG), which helps decide applicability. But there is no explicit when-to-use/when-not guidance and no routing to search_law as the alternative when a synthesized answer is not needed.

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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