get_metadataFetch the dimension definitions and code lists for a specific e-Stat statistics table (statsDataId). Returns category codes needed to construct filters for get_data. Requires _apiKey.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | J | E | |
| stats_data_id | Yes | Table ID (statsDataId) |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Input schema / examplesPrevious value: -[
- {
- "stats_data_id": "0003000010000"
- },
- {
- "lang": "E",
- "stats_data_id": "0003000010000"
- }
-]New value: +[
+ {
+ "stats_data_id": "0000150001"
+ },
+ {
+ "lang": "E",
+ "stats_data_id": "0000150001"
+ }
+]Input schema / examplesAdded value: +[
+ {
+ "stats_data_id": "0003000010000"
+ },
+ {
+ "lang": "E",
+ "stats_data_id": "0003000010000"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Response from e-Stat getMetaInfo endpoint containing dimensions and code lists",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool read-only, idempotent, and non-destructive; the description adds useful behavioral context by stating it returns dimension definitions and code lists and that it requires _apiKey. This goes beyond what annotations alone communicate without contradicting them.
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?
Three short sentences deliver the core purpose, the returned content's use, and the authentication requirement with no filler. The most important information is front-loaded, making it easy for an agent to parse quickly.
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?
For a two-parameter metadata lookup tool with a present output schema and safety annotations, the description covers the essential missing context: what the tool returns, why it is useful (get_data filter construction), and the _apiKey requirement. No critical invocation information is absent.
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 description coverage is 100%: both lang and stats_data_id have descriptions in the schema, so the baseline is 3. The description adds contextual framing around stats_data_id (identifies a specific table) and its relationship to get_data, but it does not add substantial parameter-level detail beyond the schema.
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 states a specific verb ('Fetch'), a clear resource ('dimension definitions and code lists for a specific e-Stat statistics table'), and distinguishes this metadata tool from data-retrieval tools like get_data. The mention of 'statsDataId' and its downstream use for get_data makes the tool's role unambiguous.
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 clearly positions this tool as a prerequisite step for constructing filters for get_data, giving the agent a concrete use context. It does not explicitly state when not to use it or name alternative metadata/catalog tools, but the context is clear enough for routing.
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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