dataset_provenanceThe source, the date it was computed, the licence and the citation for the Stagenix 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 burden. 'Read this' implies a read-only retrieval, and the listed return fields clarify what the tool exposes. However, it does not explicitly state that there are no side effects, nor does it address access or output details beyond the listed fields.
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?
One sentence covers the tool's content, purpose, and usage context with no filler. The title and description work together efficiently, and every word contributes to understanding what the tool does.
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 retrieval tool with no output schema, the description is fully sufficient. It tells the agent what the tool returns, why to use it, and when it applies. No additional information is needed 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?
The input schema has zero parameters, so the baseline is 4. The description correctly adds no parameter-related claims because none exist; it would be inappropriate to penalize for missing parameter documentation.
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 names the exact resource (Stagenix dataset provenance) and the exact content returned: source, computed date, licence, and citation. It also gives the practical purpose ('attribute a figure correctly'), making the tool's function unmistakable and distinct from the sibling dataset 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?
'Read this to attribute a figure correctly' clearly states when the tool should be used. It does not explicitly mention alternatives or exclusions, but the provenance focus makes it contextually obvious that this is not for data viewing or search operations.
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.
Most tools have clear, distinct purposes—schema, provenance, exact-match lookup, substring search, comparisons, statistics, and ranking. dataset_compare and dataset_row overlap somewhat since both filter by column values, but compare is explicitly for ordered multi-value 'X vs Y' queries while row is for a single exact match.
All tools share a consistent dataset_ prefix and use clear, descriptive lowercase names. Even though some are nouns (columns, row, provenance) and others are verbs (compare, search, stats, top), the pattern is uniform and predictable.
Seven tools is well-scoped for a dataset querying server. Each tool covers a distinct mode of interaction—schema discovery, provenance, exact lookup, search, comparison, statistics, and ranking—without unnecessary redundancy.
The tool surface covers the core read-only dataset exploration lifecycle well: understand schema, get provenance, retrieve rows, search, compare, compute statistics, and rank. A minor gap is the absence of a tool for fetching distinct values or arbitrary aggregations, but this is not a critical dead end for the stated purpose.