table_dimensionsColumn/dimension definitions for a table (DataProperties): what each field means, its key, type, unit and decimals. Use this before get_data to learn which $select keys and $filter dimensions exist.
| Name | Required | Description | Default |
|---|---|---|---|
| table | Yes | CBS table id, e.g. "37296eng". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "table": "37296eng"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety and side effects. The description adds behavioral context about how to use the returned definitions ($select and $filter), which is useful beyond the structured fields. It does not describe pagination or error behavior, but for a read-only metadata tool this is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and followed by usage guidance. Every sentence earns its place with no redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read-only tool, the description fully explains what it returns (key, type, unit, decimals) and how it fits into the workflow (before get_data). No output schema exists, but the description compensates by summarizing the return contents. Sibling context is not needed for completeness here.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'table' is fully described in the schema with an example ('CBS table id, e.g. "37296eng"'), yielding 100% schema description coverage. The tool description does not add additional parameter-specific detail beyond the schema, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides 'Column/dimension definitions for a table (DataProperties)' and specifies what each field means, its key, type, unit and decimals. It distinguishes itself from siblings like get_data by positioning it as a prerequisite step, 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs 'Use this before get_data to learn which $select keys and $filter dimensions exist,' providing a clear usage context and workflow guidance. However, it does not explicitly contrast with other metadata tools like dimension_values or table_info, so it lacks a full when-not-to-use statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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).
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.