dataset_provenanceThe source, the date it was computed, the licence and the citation for the Vatnix 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 full behavioral burden. It discloses the exact informational content and frames the operation as read-only via 'Read this'. Side effects are not mentioned, but none are plausibly expected for a zero-parameter metadata lookup.
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 tight sentences with no wasted words. The first sentence enumerates content; the second states the use case. The title adds a concise complement.
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, no-output-schema tool, the description is complete. It lists the returned fields and the intended use, which is everything an agent needs to call it and interpret the result.
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 input schema has zero parameters, so there are no parameter meanings to clarify. The description adds no parameter information, which is appropriate and matches the baseline for parameterless tools.
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?
States exactly what the tool returns: source, computed date, licence, and citation for the Vatnix dataset. The title reinforces the purpose, and the resource is specific enough to distinguish it from sibling data-manipulation tools.
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 instruction 'Read this to attribute a figure correctly' gives a clear when-to-use trigger. It does not explicitly name alternatives or exclusions, but with zero parameters and a unique metadata purpose, the guidance 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 serves a unique function: schema inspection, row retrieval, search, stats, top/bottom, value comparison, and provenance. There is no overlap or ambiguity between them.
All tools follow the consistent pattern 'dataset_<operation>' with lowercase snake_case. The operation is a clear noun or verb describing the function, making the naming predictable and intuitive.
With 7 tools, the set is well-scoped for dataset exploration. Each tool covers a distinct aspect of data access and analysis, and none feels redundant or unnecessary.
The tool surface covers the essential operations for working with a dataset: schema discovery, exact matching, full-text search, summary statistics, extreme values, comparisons, and provenance. This is a complete lifecycle for typical exploratory questions.