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Statec Lu

by io.github.pipeworx-io

List Dataflows

list_dataflows
Read-onlyIdempotent

Browse or keyword-search STATEC (Luxembourg statistics) datasets, called "dataflows". Each result has an id (e.g. "DF_A1100", the dataflowRef you pass to get_data / dataflow_structure) and an English name plus a short description (publication date, periodicity, author, category). STATEC publishes hundreds of datasets, so pass query to filter unless you really want the whole catalog. Example: list_dataflows({ query: "population" }) or list_dataflows({ query: "unemployment" }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default 50).
queryNoCase-insensitive substring filter on dataset id, name, or description, e.g. "population", "inflation", "GDP", "wages".

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": "population"
+  },
+  {
+    "limit": 20,
+    "query": "unemployment"
+  }
+]
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses result contents (`id`, English name, description, publication date, periodicity, author, category), the size of the catalog, and the query-filtering behavior. This provides meaningful context about what the agent will receive and how to navigate the dataset space.

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 focused sentences, front-loaded with the core purpose, and includes practical examples. Every sentence adds useful information without fluff or repetition of schema details.

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?

Even without an output schema, the description explains what each result contains, how the `id` connects to other tools, and how to filter results. The examples and catalog-size warning give the agent enough context to decide when and how to invoke the tool.

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?

The input schema already fully documents both parameters with clear descriptions, so baseline is 3. The description adds value by giving usage advice ('pass `query` to filter unless you really want the whole catalog'), concrete examples, and explaining the meaning of the returned `id` in relation to other tools.

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 specific verbs ('Browse or keyword-search') and names the resource (STATEC datasets called dataflows). It distinguishes the tool from siblings by explicitly stating that the returned `id` is the dataflowRef to pass to `get_data` / `dataflow_structure`.

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 clear guidance on when to use the tool, advises filtering with `query` to avoid the whole catalog, and provides concrete examples. It also references sibling tools (`get_data`, `dataflow_structure`) and explains how the output relates to them, enabling correct tool chaining.

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
Disambiguation3/5

The three ask_pipeworx variants (stable, beta, grounded) plus deep_research and validate_claim create real selection ambiguity — an agent could easily pick the wrong one. Many other tools (entity_profile, bet_research, scan_dependency) are clearly distinct, but the overlapping meta-query tools muddy the boundary.

Naming Consistency3/5

Naming is a mix of verb-initial (get_data, resolve_entity, generate_llms_txt, scan_dependency) and noun-initial (dataflow_structure, entity_profile, polymarket_edges, pipeworx_trending) conventions. The ask_pipeworx family and Polymarket cluster are internally consistent, but there is no single predictable pattern across the set.

Tool Count2/5

34 tools is heavy, and the server named 'Statec Lu' (Luxembourg statistics) carries 30+ tools for prediction markets, npm dependencies, AI visibility, memory, and subscriptions. It reads as an everything-server rather than a focused statistics integration; most tools have nothing to do with STATEC.

Completeness4/5

Within the STATEC domain, list_dataflows → dataflow_structure → get_data is a complete browse-and-query workflow. The broader domains also have good coverage (memory save/recall/forget, subscription list/create/cancel, rich Polymarket research tools). Minor gaps like no data-format conversion or direct 'latest value' shortcut exist, but they are workable.

Resources