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Glama

Statbel Be

by io.github.pipeworx-io

List Datasets

list_datasets
Read-onlyIdempotent

Browse/search Statbel datasets (be.STAT "datasources"). Each dataset has a name code (e.g. "IM_EAF_HOUSE_SALES_IDX"), a descriptions map with one entry per supported language (nl/fr/de/en), a category id, supportedLocales, and last-update timestamps. Filter by case-insensitive substring matched against the dataset code and all-language descriptions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default 50, max 500).
queryNoCase-insensitive substring matched against the dataset code and any-language description. Omit to list all.

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": "IM_EAF_HOUSE_SALES_IDX"
+  },
+  {
+    "limit": 100,
+    "query": "population"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is known. The description adds valuable behavior beyond this: substring matching across code and all-language descriptions, and a list of fields each dataset contains (name, descriptions map, category id, supportedLocales, last-update timestamps). No contradiction with annotations.

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 two sentences, front-loaded with the core purpose, and every sentence earns its place. The first sentence states the action and resource; the second explains the dataset structure and filtering. No unnecessary words.

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?

This is a simple list tool with two optional parameters, fully documented in the schema. The annotations cover the safety profile, and the description explains the filtering behavior and the fields in each returned dataset, effectively substituting for an output schema. This is 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?

The input schema provides 100% coverage of the two parameters with clear descriptions. The description adds no additional parameter semantics beyond what the schema already states, so the baseline score of 3 is appropriate for high schema coverage.

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 opens with 'Browse/search Statbel datasets', clearly identifying the action and resource. It further details the dataset structure and filtering behavior, distinguishing it from sibling tools like get_dataset or list_views. This is a specific verb+resource with sibling differentiation.

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 implies the usage context: browse/search datasets with optional case-insensitive substring filtering. It gives explicit filter semantics but does not mention alternatives or when not to use this tool. This is clear context without exclusions, earning a 4 rather than a 5.

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
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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