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Glama

Cbs Nl

by io.github.pipeworx-io

Get Data

get_data
Read-onlyIdempotent

Fetch observations from a table (TypedDataSet). Supports OData v3 query params. Use $select to pick columns (from table_dimensions keys) and $filter to subset, e.g. filter="Periods eq '2020JJ00'". Always set a $top to avoid huge responses.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNoOData $top, max rows to return (default 50).
skipNoOData $skip, rows to skip for paging.
tableYesCBS table id, e.g. "37296eng".
filterNoOData $filter, e.g. "Periods eq '2020JJ00'" or "substringof('2020',Periods)".
selectNoOData $select, comma-separated dimension/topic keys, e.g. "Periods,TotalPopulation_1".

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: +[
+  {
+    "select": "Periods,TotalPopulation_1",
+    "table": "37296eng",
+    "top": 100
+  },
+  {
+    "filter": "Periods eq '2023JJ00'",
+    "select": "Periods,TotalPopulation_1",
+    "table": "37296eng",
+    "top": 50
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already indicate a safe read operation (readOnlyHint, idempotentHint, destructiveHint false). The description adds valuable behavioral insight, notably the warning that without $top, responses can be huge. It also clarifies that $select should derive keys from table_dimensions, which aids correct invocation.

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?

Two sentences, efficiently packed with purpose and key usage guidance. The example filter string is concrete. No filler or redundant content.

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?

The tool is straightforward, and the description covers core purpose and critical usage tips. No output schema exists, but the description doesn't fully explain return format or pagination beyond $top/$skip hints in the schema. Still, it is sufficient for typical usage and does not leave major gaps.

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 covers 100% of parameters with examples, so baseline is 3. The description enhances meaning by explaining how $select relates to table_dimensions keys and by advising $top usage to avoid large responses, adding practical semantics beyond the field names.

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 it fetches observations from a table (TypedDataSet), which is a specific verb+resource combination. It distinguishes itself from sibling tools like table_dimensions and table_info by focusing on data retrieval and explicitly mentioning OData query params for selecting and filtering.

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?

Provides clear usage context: how to use $select (from table_dimensions keys), $filter (with example), and the recommendation to always set $top. However, it does not explicitly contrast with alternative tools or state when to prefer this over siblings, only referencing table_dimensions for keys.

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

A3.8/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions with subtle differences that are hard to distinguish (beta is currently identical to the stable version). Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_fill_risk) similarly overlap in opportunity-finding. Entity_profile, compare_entities, and recent_changes also share company-research territory.

Naming Consistency3/5

All tool names use snake_case, which is consistent, but the structural pattern varies widely: some are verb_noun (get_data, search_tables), some are noun_phrase (table_dimensions, entity_profile), some are brand-prefixed (pipeworx_trending, polymarket_edges), and the memory tools (remember, recall, forget) break the pattern entirely. Mixed conventions make the set feel less coherent.

Tool Count2/5

With 36 tools, the set is heavy, and the server name 'Cbs Nl' implies a focused CBS statistics dataset, yet most tools cover unrelated domains (Polymarket, AI visibility, npm dependencies). Even as a general data-research platform, the count exceeds the 25-tool threshold for 'heavy', and many tools could be consolidated (e.g., the three ask_pipeworx variants).

Completeness4/5

As a general data-research platform, the tool surface is fairly complete: discovery (discover_tools, search_tables, suggest_questions), metadata (table_info, table_dimensions), retrieval (get_data, ask_pipeworx, deep_research), validation (validate_claim, compare_entities), and supporting features (memory, subscriptions, feedback). Minor gaps include no explicit tool to manipulate data or manage sources, but for a read-heavy research assistant, the coverage is strong.

Resources