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Nsi Bg

by io.github.pipeworx-io
OverviewAdmin

List Datasets

list_datasets
Read-onlyIdempotent

List NSI Bulgaria open-data datasets with their numeric id and English (or Bulgarian) name. Each dataset id can be passed to get_dataset for the actual figures. Use the optional filter to substring-match dataset names (case-insensitive), e.g. 'population', 'export', 'inflation'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLanguage of dataset names. Default en.
filterNoCase-insensitive substring to match against dataset names.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "filter": "population"
+  },
+  {
+    "filter": "inflation",
+    "lang": "bg"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds that it returns dataset id and name, and that filter is case-insensitive. This adds some context but does not significantly extend beyond the annotations.

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 concise at 2-3 sentences, front-loaded with the main purpose, and uses a clear structure without unnecessary details.

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?

Although there is no output schema, the description specifies that the tool returns dataset id and name, and references the follow-up tool. It is complete for a simple listing tool, though pagination or size limits are not mentioned (not critical here).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds value by explaining the 'filter' parameter as a case-insensitive substring match and providing examples. It also clarifies the default for 'lang'. This goes beyond the schema alone.

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 lists NSI Bulgaria open-data datasets with numeric id and English/Bulgarian name, and distinguishes it from the sibling 'get_dataset' by noting that the id can be passed there for actual figures.

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 explains when to use the tool (to list datasets) and provides guidance on using the optional 'filter' parameter with examples. It does not explicitly state when not to use it or list alternatives, but the context is clear.

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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