dataset_provenanceThe source, the date it was computed, the licence and the citation for the Mandatzo 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?
With no annotations, the description carries the burden, and it does so by naming the fields returned and framing the tool as a read/attribution action. It does not explicitly say 'read-only', but 'Read this' and the metadata-only purpose make the behavior clear.
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 concise sentences, front-loaded with the returned data and ending with the use case. There is no filler, repetition, or unnecessary detail.
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 provenance lookup, the description is complete: it names the dataset, the specific provenance fields, and the reason to call it. Nothing operationally important is missing.
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 has zero parameters, so the baseline is 4 and there is no parameter documentation burden. The description instead clarifies the content of the provenance information, which is the only relevant semantic detail here.
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 states exactly what the tool provides: source, computation date, licence, and citation for the Mandatzo dataset, and connects it to the action of attributing a figure correctly. This clearly distinguishes it from sibling dataset tools such as dataset_stats or dataset_search.
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 gives a clear context for use: when you need to attribute a figure correctly. It does not explicitly list alternatives or exclusions, but for a zero-parameter provenance lookup the intended situation is obvious.
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 clearly distinct purposes: schema, provenance, exact lookup, substring search, multi-value comparison, stats, and top/bottom. The only mild overlap is among dataset_row, dataset_compare, and dataset_search, but their matching semantics are different enough to avoid serious confusion.
All tools share the consistent dataset_ prefix and use short, descriptive names like search, stats, and compare. Minor deviations exist because dataset_columns, dataset_provenance, and dataset_row are nouns rather than verb-led names, but the overall pattern remains predictable.
Seven tools is a well-scoped set for a single-dataset query server. Each tool addresses a different common question type, and none feels redundant or excessive.
The toolkit covers schema discovery, provenance, exact and fuzzy row lookup, comparisons, numeric statistics, and top/bottom queries. It lacks some advanced capabilities like arbitrary numeric filtering or group-by aggregation, but the core needs for querying the Mandatzo dataset are well covered.