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Statec Lu

by io.github.pipeworx-io

Dataflow Structure

dataflow_structure
Read-onlyIdempotent

Get the structure (Data Structure Definition) of one STATEC dataset: its ordered dimensions and, for each, the valid codes. Use this BEFORE get_data to learn how to build the dot-separated SDMX key. The key has one position per dimension, in dimension_order; an empty position is a wildcard. Example: dataflow_structure({ dataflow_id: "DF_A1100" }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataflow_idYesSTATEC dataflow id from list_dataflows, e.g. "DF_A1100".

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: +[
+  {
+    "dataflow_id": "DF_A1100"
+  }
+]
  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=true, idempotentHint=true, and destructiveHint=false. The description adds valuable detail about the output (ordered dimensions with valid codes) and the wildcard behavior for empty key positions, going beyond annotation declarations. No contradiction.

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?

Three sentences, front-loaded with the core purpose, followed by a practical usage note and a concrete example. Every sentence contributes meaningful information without redundancy.

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?

Given a simple one-parameter tool with rich annotations, the description fully covers when and how to use it. It explains the key concept (dimension_order, wildcards) necessary for effective use and is complete in context.

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?

The input schema already documents dataflow_id as a STATEC dataflow id from list_dataflows, giving 100% coverage. The description adds an example invocation but no new parameter semantics beyond what the schema provides, so 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 clearly states the tool retrieves the structure (dimensions and valid codes) of a STATEC dataset. It explicitly positions it as a precursor to get_data, which distinguishes it from sibling tools like get_data and list_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?

Description explicitly instructs to use this tool BEFORE get_data, providing clear temporal context and a specific purpose (building the dot-separated SDMX key). This is strong practical guidance with no ambiguity.

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