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Ansvar-Systems

Latvian Data Protection MCP

lv_dp_search_decisions

Search Latvian data protection decisions and sanctions by keyword, topic, or decision type. Retrieve references, entity names, fine amounts, and GDPR articles cited.

Instructions

Full-text search across DVI (Datu valsts inspekcija) decisions and sanctions. Returns matching decisions with reference, entity name, fine amount, and GDPR articles cited.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoFilter by decision type. Optional.
limitNoMaximum number of results to return. Defaults to 20.
queryYesSearch query (e.g., 'sīkdatnes', 'darbinieku uzraudzība', 'datu pārkāpums')
topicNoFilter by topic ID (e.g., 'consent', 'cookies', 'data_breach'). Optional.
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. It indicates a non-destructive read operation via 'search' and 'returns,' and names the output fields. However, it does not disclose default limit behavior, pagination, or any access requirements. For a lightweight read tool, this is adequate but not rich.

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 a single, front-loaded sentence. It starts with the action and resource, then lists return fields—no filler, every word serves a purpose.

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 absence of an output schema, the description's list of return fields (reference, entity name, fine amount, GDPR articles) provides necessary context. It does not mention default limit or pagination, but those are covered by the schema's limit param. The tool is straightforward, and the description covers the essential information an agent needs.

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%, with each parameter already described. The description adds the 'full-text' qualifier, which informs how the query parameter is interpreted, but otherwise adds no new parameter details beyond what the schema provides. Thus, it meets the baseline.

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 opens with a specific verb 'Full-text search' and names the exact resource 'DVI (Datu valsts inspekcija) decisions and sanctions.' This clearly differentiates it from sibling tool lv_dp_search_guidelines, which searches guidelines. It also lists concrete return fields, making the tool's scope 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 description clearly establishes the context for use: searching decisions and sanctions. While it doesn't explicitly state 'use this instead of lv_dp_search_guidelines for decisions,' the resource distinction is implied strongly enough that an agent can infer when to select this tool over its siblings.

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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