search_datasetsSearch Netherlands Open Data (Netherlands) for datasets by keyword. Returns each dataset's id/name, title, organization, and its resources (each with a resource_id for query_resource).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-100, default 20). | |
| query | No | Keyword(s); blank/"*" lists all. | |
| offset | No | Pagination offset. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "housing"
+ },
+ {
+ "limit": 50,
+ "offset": 0,
+ "query": "population"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, etc. The description adds context about the data source and specific return structure. No contradictions. Could add more on pagination or rate limits but adequate.
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 concise sentences: one for purpose, one for return structure. No fluff or redundancy.
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 search tool with 3 optional parameters and no output schema, the description provides adequate context on what is returned and how to chain with query_resource. Could mention pagination behavior but schema covers it.
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 coverage is 100% with clear descriptions for each parameter (limit, query, offset). The description adds no new parameter-level detail beyond the schema, meeting baseline.
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 (search), the resource (Netherlands Open Data datasets by keyword), and the specific return fields including resource_id for chaining to query_resource. This distinguishes it from sibling tools like query_resource and search_within.
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 implies when to use it (searching datasets by keyword) and mentions that returned resource_id is for query_resource, suggesting a follow-up. Lacks explicit 'when not to use' but context is clear.
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.