get_dataset_metadataRetrieve full schema and metadata for a Nova Scotia Open Data dataset by its 8-character identifier (e.g. '3nka-59nz'). Returns all column field names, data types, and descriptions — essential before calling query_dataset so you know the exact field names to use in $select and $where clauses.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset_id | Yes | 8-character Socrata dataset identifier (e.g. '3nka-59nz') |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the return content (column field names, data types, descriptions) and positions the tool as a prerequisite for querying. However, it does not disclose potential limitations, error conditions, or whether any side effects or permissions are involved. The safety profile is implied but not explicit, so a mid-range score is appropriate.
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 long, with the key action and resource placed upfront. Every clause adds value: the identifier format, the return content, and the critical usage context (before query_dataset). There is no redundancy or filler.
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 one-parameter metadata retrieval tool, the description covers what it does, what it returns, and why it matters (for query_dataset). It does not detail error handling or output structure, but given the simplicity of the tool and the presence of a clear usage narrative, it is sufficiently complete for an agent to invoke it 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 coverage is 100% for the single parameter, and the schema description already includes the 8-character Socrata identifier format with an example. The description repeats the example and adds context about Nova Scotia Open Data, but it does not meaningfully extend the semantics beyond what the schema provides. This matches the baseline of 3 for high schema coverage.
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 opens with a specific verb ('Retrieve') and a clear resource ('full schema and metadata for a Nova Scotia Open Data dataset'), with a concrete identifier format. It clearly distinguishes from sibling tools list_categories, query_dataset, and search_datasets by focusing on metadata retrieval rather than data querying, searching, or category listing.
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 states when to use this tool: 'essential before calling query_dataset', and explains why (to know exact field names for $select and $where clauses). It names a specific sibling tool as the downstream consumer, providing strong contextual guidance. However, it does not explicitly mention when not to use it or compare to search_datasets/list_categories, so it stops short of the full 5.
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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Each tool has a clearly distinct purpose: listing categories, searching for datasets, retrieving metadata for a specific dataset, and querying a specific dataset. There is no overlap or ambiguity between them, and the workflow is well-defined.
All tool names follow a consistent verb_noun pattern with lowercase and underscores: get_dataset_metadata, list_categories, query_dataset, search_datasets. The verbs (get, list, query, search) are distinct and match each tool's function.
Four tools is well-scoped for a data explorer server. Each tool fills a necessary step in the workflow (discover → search → metadata → query), with no redundancy or unnecessary extras.
The tool set covers the full exploration lifecycle for open data: discovering categories, searching datasets, retrieving schema metadata, and running queries. There are no obvious gaps; the tools are designed to work together seamlessly.