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') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description discloses the primary behavior: it returns column names, data types, and descriptions. The verb 'Retrieve' signals no side effects. It doesn't mention authentication or error conditions, but for a metadata read this is acceptable.
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, front-loaded with the key purpose, and every clause adds value (format, return, use case). No 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 single-parameter read tool with no output schema, the description covers the identifier format, the return content, and the practical purpose. It is fully self-contained.
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 schema already fully documents dataset_id (100% coverage). The description adds an 8-character format and concrete example '3nka-59nz', enriching the parameter semantics beyond the schema.
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 'Retrieve full schema and metadata' – a specific verb and resource – and immediately distinguishes itself from query_dataset by stating it is 'essential before calling query_dataset'. The example identifier '3nka-59nz' makes the tool's target crisp.
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?
Explicitly states the tool is 'essential before calling query_dataset' to learn exact field names for $select and $where clauses, giving a clear when-to-use context. It doesn't explicitly contrast with list_categories or search_datasets, but the purpose is so distinct that no exclusion is needed.
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: metadata retrieval, category listing, querying, and searching. There is no overlap that would cause an agent to select the wrong tool.
All tool names follow a consistent verb_noun pattern (get_, list_, query_, search_) and use snake_case throughout, making the set predictable and easy to navigate.
With 4 tools, the set is well-scoped for an open data explorer. Each tool covers an essential workflow step without unnecessary bloat or thinness.
The tool set covers the full exploration lifecycle: discover datasets (search, list_categories), understand their schema (get_dataset_metadata), and retrieve data (query_dataset). No obvious gaps for read-only exploration.