get_datasetFetch a single NSI Bulgaria dataset by numeric id as a JSON-stat 2.0 object (dimensions, categories, and the flat value array). Get ids from list_datasets. Example ids: 107 (children in kindergartens by municipality), 242 (aggregate replacement ratio).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Numeric dataset id, e.g. "107". | |
| lang | No | Label language. Default en. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "id": "107"
+ },
+ {
+ "id": "242",
+ "lang": "en"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds behavioral detail about the output format (JSON-stat 2.0 with dimensions, categories, value array) and the source of ids, enhancing transparency beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus example ids, with no extraneous information. Purpose is front-loaded, and every part is essential.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 adequately explains the return format. It also covers how to obtain valid ids. For a simple read tool with comprehensive annotations, this is fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and both parameters are described. The description adds real-world context for the id parameter (example datasets) and notes the default language, providing useful meaning beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a single NSI Bulgaria dataset by numeric id and returns a JSON-stat 2.0 object. It provides specific examples (ids 107 and 242) with context, distinguishing it from sibling tools like list_datasets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly tells when to use (when you have a numeric id from list_datasets) but does not explicitly state when not to use or provide alternative tools. However, the context is clear enough for an AI agent.
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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