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Ansvar-Systems

estonian-data-protection-mcp

ee_dp_search_decisions

Search AKI data protection decisions and sanctions by full text or filters, returning references, entity names, fines, and cited GDPR articles.

Instructions

Full-text search across AKI (Andmekaitse Inspektsioon) 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., 'küpsised', 'töötajate jälgimine', 'andmeleke')
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?

With no annotations, the description must carry full weight. It discloses the scope (AKI decisions/sanctions) and returns key fields, but it does not mention behavior such as pagination, sorting, default limits (though 'limit' is in schema), or potential absence of results. The 'full-text' aspect is useful but limited.

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 consists of two short sentences, front-loading the core purpose and then listing return fields. Every sentence adds value, and there is no redundancy or filler.

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 there is no output schema, the description reasonably lists the key return fields and clarifies scope. It does not explain error handling or filter interactions, but for a search tool of moderate complexity, it is mostly self-sufficient. Sibling tool names further contextualize its role.

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 all four parameters with clear descriptions and examples. The tool description adds no additional parameter semantics beyond what is in the schema, so the baseline score of 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 clearly states the tool performs 'Full-text search across AKI decisions and sanctions' and lists the returned fields (reference, entity name, fine amount, GDPR articles cited). This distinguishes it from siblings like ee_dp_get_decision (which retrieves a single decision) by focusing on search rather than retrieval.

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 implies usage for searching decisions and sanctions, and the return fields make it clear this is for finding matches. However, it does not explicitly state when to prefer this over ee_dp_get_decision or ee_dp_search_guidelines, nor does it mention exclusions or prerequisites, leaving a small gap in guidance.

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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