query_resourcePull rows from a tabular Netherlands Open Data resource (CKAN DataStore) by resource_id (from a dataset's resources). Optional full-text q, limit, offset. Note: only resources with the DataStore enabled are queryable.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Optional full-text filter over the rows. | |
| limit | No | Max rows (default 100). | |
| offset | No | Pagination offset. | |
| resource_id | Yes | A resource_id from dataset/search_datasets. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "abc123def456"
+ },
+ {
+ "limit": 50,
+ "q": "Amsterdam",
+ "resource_id": "xyz789uvw012"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by noting the DataStore requirement, which is a behavioral constraint beyond annotations. No contradictory information.
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?
Two sentences, no redundancy. The main action and key constraint are front-loaded. Every word serves a purpose.
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 read-only tool with no output schema, the description covers the essential: what resource_id is, what optional filters exist, and the DataStore requirement. It provides sufficient context for an agent to use the tool correctly.
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 the schema already documents all parameters. The description repeats 'Optional full-text q, limit, offset' but does not add deeper semantic meaning beyond what is in the schema. Baseline 3 is appropriate.
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 action (pull rows), the specific data source (Netherlands Open Data CKAN DataStore), and the key parameter (resource_id). It distinguishes itself from sibling tools like search_datasets by focusing on querying a specific resource's data.
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?
It mentions optional parameters (q, limit, offset) and includes a crucial usage condition: only DataStore-enabled resources are queryable. However, it does not explicitly compare to alternative sibling tools like search_within or mention when not to use it.
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 tool families have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates (the beta is currently identical), and the six polymarket_* tools plus bet_research heavily overlap in prediction-market analysis. ai_visibility_check and scan_competitor_ai_presence also serve the same core function. An agent would frequently need to read lengthy descriptions to pick the right one, and could easily misselect.
Most tools follow a readable snake_case pattern, but the style is mixed: some are verb-first (ask_pipeworx, search_datasets, resolve_entity), some are domain-prefixed nouns (polymarket_edges, pipeworx_feedback), and a few are bare nouns or adjective-noun phrases (dataset, entity_profile, recent_alerts). It is not chaotic, but there is no single predictable verb_noun convention across the set.
With 34 tools, this is above the 25+ threshold considered too many for a coherent toolset. The count is inflated by near-duplicate families (three ask_pipeworx variants, six polymarket tools) that could reasonably be consolidated. While the server covers a broad domain, the number of top-level choices creates unnecessary selection burden for agents.
For a read-focused data/research gateway, the surface is quite complete: general lookup, grounded verification, deep research, entity resolution, comparisons, change tracking, memory, subscriptions, and feedback are all present. Minor gaps exist, such as no direct fetch-by-URI tool for the pipeworx:// citations that other tools return, and the Dutch open-data tools are strictly read-only. These are workarounds rather than dead ends.