get_fieldsFetch the field/dimension metadata for an NSI dataset (its column codes plus Bulgarian and English names) as parsed CSV rows. Useful for interpreting the dimension codes in a get_dataset response.
| 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": "bg"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds value by stating the output format (parsed CSV rows) and the content (column codes plus Bulgarian and English names), giving the agent a clear picture of behavior.
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?
The description is two sentences, concise and front-loaded, with no extraneous words. Every sentence adds value.
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?
Despite lacking an output schema, the description explains the return format and content sufficiently. The schema fully covers parameters, and the context signals (read-only, idempotent) are handled. Complete for a metadata retrieval tool.
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% with both parameters well-described. The description does not add additional semantic detail beyond what the schema provides, so baseline 3 is appropriate.
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's purpose: fetching field/dimension metadata (column codes and names) for an NSI dataset as parsed CSV rows. It distinguishes itself from get_dataset by noting it helps interpret dimension codes.
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 provides clear context by noting the tool is useful for interpreting dimension codes from a get_dataset response, but it does not explicitly state when not to use it or list alternatives.
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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