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Glama

Abs Au

by io.github.pipeworx-io

List Dataflows

list_dataflows
Read-onlyIdempotent

Browse or search ABS datasets (dataflows). Returns dataflow IDs + descriptive names; the ID (e.g. "CPI", "ALC", "ABS_REGIONAL_LGA2021") is what you pass to dataflow_structure and get_data. Optionally filter by a case-insensitive substring against the ID and name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default 50, max 500).
searchNoCase-insensitive substring to match against dataflow id/name (e.g. "consumer price", "labour", "population").

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: +[
+  {
+    "search": "consumer price"
+  },
+  {
+    "limit": 20,
+    "search": "labour"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and open-world hints. The description adds useful behavioral context: returns IDs and descriptive names, supports case-insensitive substring matching, and provides concrete ID examples. It does not go into pagination or edge cases, but the annotations reduce the burden.

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 three sentences, front-loaded with the main purpose and followed by essential details. Every sentence adds value—no filler or redundancy.

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?

For a simple list tool with no output schema, the description adequately covers the return concept (IDs + names) and how the output is used. It lacks explicit mention of pagination or limit behavior, but that is already in the schema. Given the tool's simplicity, this is sufficiently complete.

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 coverage is 100% with well-documented parameters. The description adds only marginal value beyond the schema, such as example IDs and the notion of case-insensitive search, which the schema already states. 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 uses a specific verb ('Browse or search') tied to a clear resource ('ABS datasets (dataflows)'). It distinguishes itself from siblings by explicitly stating that returned IDs are used with dataflow_structure and get_data, making the tool's role unambiguous.

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 clearly establishes when to use this tool: to discover dataflow IDs before querying with dataflow_structure or get_data. It gives context on the search filter but does not explicitly mention alternative listing tools or when not to use it, leaving a minor gap.

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

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