dataset_provenanceThe source, the date it was computed, the licence and the citation for the Intakez dataset. Read this to attribute a figure correctly.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
No annotations are provided, so the description carries the behavioral disclosure burden. It clearly states what the tool returns (source, computation date, licence, citation) and implies a read-only metadata lookup. It does not explicitly guarantee no side effects, but the content and phrasing make this a safe, non-mutating operation.
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 title and description are concise, front-loaded, and free of filler. Every phrase adds value: the title frames the purpose, and the description lists exactly what information the agent can expect.
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 zero-parameter metadata lookup, the description is reasonably complete: it names the dataset and enumerates the returned provenance fields. It does not specify the exact output format, but no output schema is provided and the expected content is clear enough for an agent to use the result for attribution.
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 tool accepts zero parameters, so the input schema provides no parameter documentation. With no parameters, the description does not need to explain argument semantics; the baseline of 4 applies because there is nothing to compensate for.
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 this tool provides provenance information: source, computed date, licence, and citation for the Intakez dataset. This is specific and distinct from the sibling tools, which cover columns, rows, search, statistics, comparisons, and top 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 tells the agent when to use this tool: 'Read this to attribute a figure correctly.' It gives a clear use case, though it does not explicitly discuss when not to use it or mention alternatives. Since the sibling tools are clearly unrelated to provenance, this is sufficient.
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.
Each tool targets a distinct aspect of the Intakez dataset: schema, provenance, exact lookup, substring search, aggregation, ordering, and multi-value comparison. The boundaries are clear and the descriptions specify when to use each.
All seven tools share the dataset_ prefix and use concise lowercase suffixes, creating a predictable pattern. Although some suffixes are nouns and some verbs, the convention is uniform and easy to infer.
Seven tools is a well-scoped set for dataset exploration: schema, metadata, row retrieval, search, stats, top values, and comparisons are all covered without redundancy.
The surface covers the full read-only exploration lifecycle for a dataset: understand schema and provenance, filter rows, aggregate numeric columns, and rank values. No obvious dead ends for typical dataset questions.