get_dataFetch observations from a stats table. Optionally filter by dimension codes.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | J | E | |
| limit | No | 1-100000 (default 100) | |
| filters | No | Dimension code filters as { "cdCat01":"A03503", "cdTime":"2023" } | |
| stats_data_id | Yes | Table ID (statsDataId) | |
| start_position | No | 1-based row offset |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesPrevious value: -[
- {
- "stats_data_id": "0003000010000"
- },
- {
- "filters": {
- "cdArea": "00000",
- "cdTime": "2023"
- },
- "limit": 50,
- "stats_data_id": "0003000010000"
- }
-]New value: +[
+ {
+ "limit": 3,
+ "stats_data_id": "0000150001"
+ },
+ {
+ "filters": {
+ "cdArea": "00000",
+ "cdCat02": "001"
+ },
+ "limit": 5,
+ "stats_data_id": "0000150001"
+ }
+]Input schema / examplesAdded value: +[
+ {
+ "stats_data_id": "0003000010000"
+ },
+ {
+ "filters": {
+ "cdArea": "00000",
+ "cdTime": "2023"
+ },
+ "limit": 50,
+ "stats_data_id": "0003000010000"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Response from e-Stat getStatsData endpoint containing observations",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already communicate read-only, idempotent, and non-destructive behavior, so the description does not need to restate safety. It adds only mild context ('stats table', optional filters) and does not contradict the annotations; rate limits or pagination behavior are not disclosed, but the schema covers pagination parameters.
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 one efficient sentence with the core action front-loaded and the optional behavior at the end. Every word earns its place with no fluff or repetition.
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?
With a rich input schema, examples, an output schema, and safety annotations, the description is mostly complete for invoking the tool correctly. The only notable gap is the lack of explicit routing away from search/get_metadata siblings, but the required stats_data_id and examples make invocation unambiguous.
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?
Input schema coverage is 100%, so parameters are already documented with meanings and examples. The description adds no extra parameter detail beyond 'dimension codes', which the filter property description already explains.
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 names a specific verb and resource ('Fetch observations from a stats table') and mentions optional dimension-code filtering, so an agent understands the core operation. It does not explicitly distinguish itself from siblings like search_stats or get_metadata, which prevents a 5.
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 no when-to-use or when-not-to-use guidance and never references alternative tools. 'Optionally filter by dimension codes' is about invocation, not tool selection, so an agent is left to infer when get_data is preferred over search_stats or get_metadata.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.