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Lithuanian Data Protection MCP

Lithuanian Data Protection MCP

by Ansvar-Systems
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

lt_dp_search_decisions

Search VDAI decisions and sanctions by full-text query, retrieving references, entity names, fine amounts, and GDPR articles cited.

Instructions

Full-text search across VDAI (Valstybinė duomenų apsaugos inspekcija) decisions and sanctions. Returns matching decisions with reference, entity name, fine amount, and GDPR articles cited.

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": {
      "query": "<query>"
    },
    "name": "lt_dp_search_decisions"
  }
}

Input Schema

NameRequiredDescriptionDefault
typeNoFilter by decision type. Optional.
limitNoMaximum number of results to return. Defaults to 20.
queryYesSearch query (e.g., 'slapukai', 'darbuotojų stebėjimas', 'duomenų pažeidimas')
topicNoFilter by topic ID (e.g., 'consent', 'cookies', 'data_breach'). Optional.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It states the search behavior ('full-text') and lists return fields (reference, entity, fine amount, GDPR articles), which is useful. It does not mention pagination, rate limits, or error behavior, but for a read-only search tool this is adequate.

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 concise, front-loaded sentences with no redundant information. Every word contributes to understanding what the tool does and what it returns.

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?

The tool is a straightforward search operation; the description clearly states return values, so agents know what to expect. It does not mention default limits or pagination, but this is a minor gap. The sibling lt_dp_get_decision exists for retrieving a single decision, so the overall context is reasonably complete.

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 coverage is 100%, so all four parameters are already described in the schema. The description adds no additional parameter-level semantics beyond implying the 'query' parameter is a full-text search, which the schema already conveys. Therefore 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 states a specific verb ('Full-text search') and a clear resource ('VDAI decisions and sanctions'), and distinguishes itself from sibling lt_dp_search_guidelines by explicitly naming decisions/sanctions. The return fields are also summarized, making the purpose unambiguous.

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 phrase 'Full-text search across VDAI decisions and sanctions' clearly implies this tool is for searching decision-type content. It does not explicitly mention when not to use it (e.g., use lt_dp_search_guidelines for guidelines), but the sibling tool names provide enough context for an agent to differentiate.

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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