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Glama

Estat Japan

by io.github.pipeworx-io

List Data Catalog

list_data_catalog
Read-onlyIdempotent

Browse the e-Stat high-level data catalog (dataset/table groupings) with an optional free-text filter. Returns catalog entries with dataset IDs, names, and organization metadata. This endpoint is slow at e-Stat and often exceeds its 35s budget, in which case it returns {found:false, reason:"upstream_timeout"}; search_stats answers "which Japanese statistics table covers X" faster. Requires _apiKey.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoJ (Japanese, default) | E (English)
limitNoPage size, 1-100. Defaults to 20 when omitted, so a bare call is already bounded.
queryNoOptional free-text filter, Japanese or English
data_typeNoRestrict to one file format: XLS | CSV | PDF | XML | XLS_REP | DB
stats_codeNoGovernment statistics code, e.g. "00200521" (population census)
stats_fieldNoStatistical field code, 2 digits (major) or 4 digits (minor)
survey_yearsNoSurvey period: yyyy, yyyymm, or yyyymm-yyyymm
start_positionNo1-based offset (default 1)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Changed8 schema fields changed
    • addedInput schema / properties / data_type
      Added value: +{
+  "description": "Restrict to one file format: XLS | CSV | PDF | XML | XLS_REP | DB",
+  "type": "string"
+}
    • changedInput schema / properties / lang / description
      Previous value: -"J | E"New value: +"J (Japanese, default) | E (English)"
    • changedInput schema / properties / limit / description
      Previous value: -"1-100 (default 20)"New value: +"Page size, 1-100. Defaults to 20 when omitted, so a bare call is already bounded."
    • changedInput schema / properties / query / description
      Previous value: -"Optional free-text filter"New value: +"Optional free-text filter, Japanese or English"
    • changedInput schema / properties / start_position / description
      Previous value: -"1-based offset"New value: +"1-based offset (default 1)"
    • addedInput schema / properties / stats_code
      Added value: +{
+  "description": "Government statistics code, e.g. \"00200521\" (population census)",
+  "type": "string"
+}
    • addedInput schema / properties / stats_field
      Added value: +{
+  "description": "Statistical field code, 2 digits (major) or 4 digits (minor)",
+  "type": "string"
+}
    • addedInput schema / properties / survey_years
      Added value: +{
+  "description": "Survey period: yyyy, yyyymm, or yyyymm-yyyymm",
+  "type": "string"
+}
  2. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "労働"
+  },
+  {
+    "lang": "E",
+    "limit": 15
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "description": "Response from e-Stat getDataCatalog endpoint containing table groupings",
+  "type": "object"
+}
  3. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description reveals important behavior beyond annotations: the endpoint is slow, can exceed its 35s budget, and returns a specific timeout response shape. It also notes the _apiKey requirement, providing operational context annotations do not cover.

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?

Three sentences with no filler: purpose, key behavioral caveat, and auth requirement. The most important operational warning is front-loaded in the second sentence, and every sentence adds distinct value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the rich schema, output schema, and annotations, the description covers the essential context: purpose, alternative tool, failure behavior, and authentication. Nothing critical is missing for an agent to call this tool correctly.

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 input schema already explains all eight parameters. The description adds little semantic detail beyond labeling the query as a free-text filter, which is already present in the schema. Baseline 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 names a specific verb ('Browse') and resource ('e-Stat high-level data catalog'), and clarifies the scope as dataset/table groupings with an optional free-text filter. It also distinguishes itself from search_stats, making the tool's purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states that search_stats answers 'which Japanese statistics table covers X' faster, giving the agent a clear alternative for lookups while positioning this tool for browsing. It also warns about the timeout risk, which directly informs selection.

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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TDQS

A3.6/5.0
Disambiguation3/5

Many tools have overlapping purposes, especially the ask_pipeworx variants and Polymarket tools, but detailed descriptions help differentiate. Some tools like 'discover_tools' and 'suggest_questions' also have similar discovery roles, causing potential confusion.

Naming Consistency3/5

Names follow snake_case but lack a consistent pattern: some start with verbs (e.g., 'ask_pipeworx', 'compare_entities'), others with nouns (e.g., 'entity_profile', 'recent_changes'), and prefixes like 'pipeworx_' and 'polymarket_' are used sporadically, making the naming scheme mixed but still readable.

Tool Count2/5

35 tools is excessive for a server named 'Estat Japan', which should focus on Japanese statistics. The majority of tools are general-purpose Pipeworx tools, diluting the scope and making the count feel bloated for the stated purpose.

Completeness2/5

The e-Stat tools (list_data_catalog, search_stats, get_metadata, get_data) provide basic read-only access but lack update or delete operations. The inclusion of many unrelated tools leaves significant gaps for Japanese statistics, and the overall surface is incomplete for the server's implied domain.

Resources