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Glama

Abs Au

by io.github.pipeworx-io

Get Data

get_data
Read-onlyIdempotent

Fetch observations from an ABS dataflow. dataKey is a dot-separated SDMX filter with one position per dimension (order from dataflow_structure); each position is a code, "+"-joined codes, or empty for wildcard. Pass "all" to fetch everything (can be large). Returns decoded series with their dimension labels and time-indexed values. Fetch dataflow_structure first to learn the dimension order and valid codes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataKeyYesDot-separated dimension filter, e.g. "1.10001.10.50.Q" or "all". Empty positions are wildcards.
endPeriodNoLatest period, e.g. "2024".
maxSeriesNoCap decoded series returned (default 100). The raw response may contain thousands.
dataflowIdYesDataflow id, e.g. "CPI".
startPeriodNoEarliest period, e.g. "2020" or "2020-Q1" or "2020-01".

Schema Changelog

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

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "dataKey": "1.10001.10.50.Q",
-    "dataflowId": "CPI",
-    "endPeriod": "2024-Q4",
-    "startPeriod": "2020-Q1"
-  },
-  {
-    "dataKey": "all",
-    "dataflowId": "LABOUR",
-    "maxSeries": 50
-  }
-]New value: +[
+  {
+    "dataKey": "1.10001.10.50.Q",
+    "dataflowId": "CPI",
+    "endPeriod": "2024-Q4",
+    "startPeriod": "2020-Q1"
+  },
+  {
+    "dataKey": "M3.3.1599.20.AUS.M",
+    "dataflowId": "LF",
+    "endPeriod": "2025-06",
+    "maxSeries": 50,
+    "startPeriod": "2025-01"
+  }
+]
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "dataKey": "1.10001.10.50.Q",
+    "dataflowId": "CPI",
+    "endPeriod": "2024-Q4",
+    "startPeriod": "2020-Q1"
+  },
+  {
+    "dataKey": "all",
+    "dataflowId": "LABOUR",
+    "maxSeries": 50
+  }
+]
  3. First observed

TDQS

A4.4/5.0
Behavior3/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds scale warning ('can be large') and return format, but it says 'Pass "all" to fetch everything' without clarifying that maxSeries (default 100) still caps decoded series. This creates a potential misunderstanding about whether 'all' bypasses the cap. No contradiction exists, but the disclosure is incomplete.

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?

Four sentences, each earning its place: purpose, dataKey syntax, 'all' option, return format, and prerequisite. Front-loaded with the core action and no redundant words. Very efficient for the complexity it conveys.

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?

The tool is complex due to dataKey construction, but the description covers the essential context: prerequisite dataflow_structure, return format (decoded series, dimension labels, time-indexed values), and scale warning. With no output schema, it gives enough return information. However, it omits the interaction between 'all' and maxSeries, which is a relevant caveat even though the schema documents maxSeries, so slight deduction from a perfect score.

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 covers all 5 parameters (100% coverage), so baseline is 3. The description significantly enhances dataKey semantics by explaining the SDMX dot-separated filter syntax, wildcards, '+'-joined codes, and the need to consult dataflow_structure for dimension order. This goes well beyond the schema's generic 'Dot-separated dimension filter' and is essential for correct invocation.

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 opens with 'Fetch observations from an ABS dataflow' – a specific verb ('fetch') plus resource ('observations') and scope ('ABS dataflow'). This clearly distinguishes it from sibling tools like dataflow_structure (which fetches dimension metadata) and list_dataflows (which lists available dataflows).

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?

The description gives explicit usage guidance: 'Fetch dataflow_structure first to learn the dimension order and valid codes,' which directly instructs the agent to use a prerequisite sibling tool. It also explains the role of dataKey and the 'all' option, making it clear when to use this tool for retrieving observations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Abs Au toolsInspect
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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but subtle overlap exists in the ask_pipeworx variants and multiple Polymarket-related tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_kalshi_spread) which could cause misselection without careful reading of descriptions.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with clear verb_noun or noun_verb structures, e.g., ai_visibility_check, compare_entities, resolve_entity. No mixing of conventions.

Tool Count3/5

With 29 tools, the server exceeds the typical ideal range of 3-15 and enters the 'too many' category. While each tool serves a specific purpose, the breadth of functionality could likely be streamlined or consolidated without losing capability.

Completeness4/5

The tool surface covers a wide range of domains—company research, fact verification, data querying, betting analysis, memory, and subscription management. However, some specialized queries rely on the generic ask_pipeworx tool rather than dedicated endpoints, leaving minor gaps for direct access.

Resources