dimension_valuesList the valid codes/labels for one dimension of a table (a sub-endpoint such as "Periods" or a region/category dimension key, as named in the table root or table_dimensions).
| Name | Required | Description | Default |
|---|---|---|---|
| top | No | Max rows (default 100). | |
| table | Yes | CBS table id, e.g. "37296eng". | |
| dimension | Yes | Dimension endpoint name, e.g. "Periods", "CaribbeanNetherlands". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "dimension": "Periods",
+ "table": "37296eng"
+ },
+ {
+ "dimension": "RegionCode",
+ "table": "37296eng",
+ "top": 50
+ }
+]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 that the tool returns codes/labels and that dimension is a sub-endpoint, providing useful behavioral context beyond the annotations. No contradictions.
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 a single, well-structured sentence that front-loads the action and resource. No unnecessary words or restatements.
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 simple listing tool with fully described parameters and strong annotations, the description is complete. It states what the tool returns (codes/labels), clarifies the dimension parameter, and references sibling tools for context, making it fully sufficient for selection and invocation.
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?
Schema description coverage is 100%, so baseline is 3. The description adds semantic meaning for the 'dimension' parameter by explaining it is a sub-endpoint named in the table root or table_dimensions, which helps clarify how to fill the parameter correctly.
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 uses a specific verb ('List') and identifies the resource ('valid codes/labels for one dimension of a table'). It clearly distinguishes from sibling tools like table_dimensions (which lists dimensions) and get_data (which retrieves data) by focusing on a single dimension's values.
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 mentions 'as named in the table root or table_dimensions', implying that users should first identify dimensions via table_dimensions or table root. This provides clear context for when to use this tool, though it does not explicitly exclude alternative tools.
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.