Skip to main content
Glama

Statbel Be

by io.github.pipeworx-io

List Views

list_views
Read-onlyIdempotent

Browse/search Statbel (Statistics Belgium, be.STAT) saved statistical views. Each view is a pre-built table over a dataset and carries a single locale (nl/fr/de/en). Returns id, name, locale, dataSourceId and publish dates. Filter by case-insensitive substring of the view name (matches in any language).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default 50, max 500).
queryNoCase-insensitive substring to match in the view name (any language). Omit to list all.
localeNoOptional filter to one language: "nl", "fr", "de" or "en".

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": "house prices"
+  },
+  {
+    "limit": 20,
+    "locale": "nl",
+    "query": "employment"
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and openWorld hints, so the bar for additional behavioral disclosure is lower. The description adds value by specifying the returned fields (id, name, locale, dataSourceId, publish dates) and the case-insensitive substring matching across languages. It could also mention pagination or rate limits, but given the annotation coverage, this is solid.

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 long, front-loaded with the primary purpose, and every sentence contributes meaning. There is no filler or redundancy, and it remains compact while covering resource context, return fields, and filter semantics.

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 moderately simple read-only list tool with three optional parameters and no output schema, the description covers the essential context: what a view is, what data is returned, and how filtering works. It does not mention default limit or sort order, but the schema documents the limit default, and the absence of an output schema is mitigated by listing the return fields. Overall, adequately complete for the tool's complexity.

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 description coverage is 100% and already explains all three parameters with examples. The description adds minimal new information beyond restating the query filter behavior and the locale values (which also appear in the schema). It does not need to compensate for a schema gap, so the baseline score of 3 applies.

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 verb+resource: 'Browse/search Statbel saved statistical views.' It clearly distinguishes from siblings like get_view (which retrieves a specific view) by describing this as a listing/search tool. It also explains the resource concept (pre-built table over a dataset), making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states the core usage (browse/search views) and mentions filter options, but does not explicitly name alternatives or when-not-to-use scenarios. The distinction from get_view and list_datasets is implied by context but not stated directly, so it falls short of explicit guidance.

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

All Statbel Be toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation2/5

The set contains several clusters of overlapping tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve broad data-query purposes, while the six Polymarket-related tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) heavily overlap in scope. The detailed descriptions help, but an agent would frequently struggle to choose the correct tool among near-synonyms.

Naming Consistency3/5

All names use snake_case, but the underlying pattern is inconsistent: list_*/get_* for Statbel, ask_* for query routers, noun-heavy names like entity_profile, bet_research, polymarket_edges, and recent_alerts, plus bare verbs like remember, recall, forget, subscribe, unsubscribe. It remains readable, but there is no single predictable verb_noun convention across the set.

Tool Count2/5

At 35 tools, the set exceeds the range where each tool clearly earns its place, and the scope is wildly broad: Belgian statistics, general data lookup, prediction markets, npm dependency checks, memory, subscriptions, llms.txt generation, and AI visibility. A server named 'Statbel Be' carries 31 tools unrelated to that name, which makes the count feel bloated and unfocused.

Completeness2/5

For the Statbel domain implied by the server name, the surface is severely incomplete: list_datasets, get_dataset, list_views, and get_view only return metadata — there is no tool to actually fetch the statistical data values. For the broader Pipeworx data-access domain, coverage is more complete, but the server's stated purpose is under-served and leaves core workflows at a dead end.

Resources