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Glama

Dst Dk

by io.github.pipeworx-io

Get Data

get_data
Read-onlyIdempotent

Pull data from a table. Provide tableId and a "variables" map of {variableCode: valueOrValues}, using codes/ids from table_info. Each value may be a single id, an array of ids, or "*" for all. Omitted variables that allow elimination are aggregated to total. format "JSONSTAT" (default, structured JSON-stat) or "BULK"/"CSV" (semicolon-delimited text).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLanguage: "en" (default) or "da".
formatNo"JSONSTAT" (default), "BULK" or "CSV" (both return semicolon CSV).
tableIdYesTable id, e.g. "FOLK1C".
variablesYesMap of variable code -> value(s). Value is a string, an array of strings, or "*". Example: {"OMRÅDE":"000","Tid":["2024K1","2024K2"],"ALDER":"IALT"}.

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: +[
+  {
+    "tableId": "FOLK1C",
+    "variables": {
+      "ALDER": "IALT",
+      "OMRÅDE": "000",
+      "Tid": "2024K1"
+    }
+  },
+  {
+    "format": "CSV",
+    "tableId": "FOLK1C",
+    "variables": {
+      "ALDER": "*",
+      "OMRÅDE": [
+        "000",
+        "101"
+      ],
+      "Tid": [
+        "2024K1",
+        "2024K2"
+      ]
+    }
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Adds useful behavioral context beyond annotations (readOnlyHint, etc.), such as aggregation of omitted variables and format details. No contradiction with 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?

Efficient single paragraph of 3 sentences, front-loaded with main purpose. No redundant or unnecessary information.

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?

Covers parameter usage and format options well. Lacks details on error handling or return structure, but given no output schema and rich annotations, adequate for typical usage.

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?

Adds meaning beyond schema by explaining variable value patterns (single id, array, '*') and aggregation semantics. Schema already has 100% description coverage, so baseline 3; description adds extra value.

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?

Description clearly states 'Pull data from a table' with specific verb and resource. It distinguishes from sibling tools like table_info (metadata) and list_tables (listing tables) by focusing on data retrieval.

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?

Provides clear context on when to use: provide tableId and variables map. Explains format options and behavior for omitted variables. However, lacks explicit when-not-to-use or comparisons to 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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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have overlapping functionality, especially in the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the polymarket group (bet_research, polymarket_edges, etc.). Descriptions acknowledge these overlaps, making it difficult for an agent to choose the correct tool without deep understanding.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use snake_case (ask_pipeworx, bet_research), others use camelCase or PascalCase (ai_visibility_check, generate_llms_txt, polymarketArbitrage?). Also, tools like 'list_subjects' and 'get_data' have no clear pattern with the rest.

Tool Count2/5

With 35 tools, the server feels overloaded and tries to cover too many distinct domains (data queries, prediction markets, subscriptions, memory, national statistics). This broad scope reduces coherence and makes the tool set harder to navigate.

Completeness3/5

The tool set covers a wide range of capabilities, from data retrieval to prediction market analysis and subscription management. However, the inclusion of Denmark-specific statistics tools (e.g., list_subjects, get_data) seems out of place and creates a niche gap for users interested in other national datasets.

Resources