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Glama

Ilostat

by io.github.pipeworx-io

List Dataflows

list_dataflows
Read-onlyIdempotent

Browse or keyword-search ILOSTAT datasets (dataflows) from the International Labour Organization. Each result has an id (the dataflowId you pass to get_data / dataflow_structure) and an English name. ILO publishes ~1,200 datasets covering employment, unemployment, labour force participation, wages, hours worked, informality, working poverty, child labour, and occupational safety. Always pass query to filter unless you really want the whole list. Example: list_dataflows({ query: "unemployment rate" }) or list_dataflows({ query: "minimum wage" }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default 50).
queryNoCase-insensitive substring filter on dataset id or name, e.g. "unemployment", "wages", "labour force".

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: +[
+  {
+    "query": "unemployment rate"
+  },
+  {
+    "limit": 20,
+    "query": "wages"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the description doesn't need to repeat those. It adds useful behavioral context: the domain (ILO datasets), result structure (id and English name), and query behavior (case-insensitive substring). No contradictions.

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?

Extremely concise: two sentences plus an example. Every sentence earns its place—purpose, guidance, and context are front-loaded. No unnecessary fluff.

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?

For a list/search tool with high-quality annotations and full schema coverage, the description is complete. It explains the domain, how to filter, and how results connect to sibling tools. No gaps given the complexity.

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 coverage is 100%, so the schema explains the parameters. The description adds value by giving examples and clarifying that query is a case-insensitive substring filter, and advising to use it for filtering. This enhances understanding beyond the schema.

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 it browses/keyword-searches ILOSTAT datasets, specifying the resource (ILO datasets) and the verb (list/search). It distinguishes from sibling tools like get_data and dataflow_structure by noting that the result's id is used for those tools.

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?

Explicitly advises to always pass query unless the full list is needed, and provides concrete examples. While it doesn't state when NOT to use the tool versus alternatives, the context about filtering is clear and actionable.

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

A3.9/5.0
Disambiguation2/5

The server mixes three near-identical ask_pipeworx variants (stable, beta, grounded) where beta is currently described as functionally identical to stable, plus several overlapping discovery and research tools (discover_tools, suggest_questions, deep_research, validate_claim, ask_pipeworx). Multiple entity/comparison/change tools (entity_profile, compare_entities, recent_changes) and several Polymarket tools further blur boundaries, requiring careful reading of long descriptions to pick correctly.

Naming Consistency3/5

Many tools follow a clear verb_noun snake_case pattern (list_dataflows, get_data, compare_entities, validate_claim, resolve_entity), and the polymarket_* prefix groups the prediction-market family consistently. However, naming is mixed: bare verbs (remember, forget, recall), noun phrases (dataflow_structure, entity_profile), brand-prefixed tools (pipeworx_feedback, pipeworx_trending), and inconsistent verb choices like ask_pipeworx vs ask_pipeworx_grounded vs suggest_questions.

Tool Count2/5

34 tools is heavy for a server named Ilostat, especially since only three tools (list_dataflows, dataflow_structure, get_data) actually serve ILOSTAT data. The rest form a broad general-purpose data/prediction-market platform that appears bolted on rather than scoped to the server's stated identity.

Completeness4/5

For the ILOSTAT domain specifically, the read-only lifecycle is complete: list_dataflows discovers datasets, dataflow_structure explains dimensions/codes, and get_data retrieves observations — no obvious dead ends for public data access. Other embedded subsystems (memory, subscriptions) also have full CRUD, though the overall server lacks a coherent single-domain surface to judge against.

Resources