dataset_provenanceThe source, the date it was computed, the licence and the citation for the Rapportvo 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 burden of behavioral disclosure. It does describe what the tool returns (source, date, licence, citation) and implies a read-only nature through 'Read this', but it never explicitly states the operation is safe or non-mutating. The risk is low given zero parameters and an informational purpose, but a direct statement of non-destructiveness would strengthen it.
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 short sentences with zero filler: the first lists the delivered content types, the second gives the use case. The information is front-loaded and every word earns its place.
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 tool with no output schema, the description covers the essentials: which dataset, what information is returned (source, date, licence, citation), and when to use it. Minor gaps remain around the exact format or shape of the citation, but the content list is sufficient for an agent to invoke this tool 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 tool has zero parameters, so the baseline is 4 and there is no semantic gap for the description to fill. The description correctly spends its budget on return content and usage rather than parameters, which is the appropriate allocation for a no-input tool.
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 content delivered — source, computation date, licence, and citation — tied to the Rapportvo dataset, so an agent knows precisely what this tool returns. The verb is implicit ('Read this' rather than 'Returns...'), which keeps it a step below a fully explicit verb+resource statement, but the content list and title make the purpose unambiguous and distinguish it from all sibling 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' gives a clear, actionable condition for when to invoke this tool. It does not explicitly name alternatives or exclusions, but none are needed — the siblings (columns, compare, row, search, stats, top) are functionally distinct, so the correct choice between them is evident.
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 are clearly distinct query modes, but dataset_columns includes a provenance banner that slightly overlaps with dataset_provenance, and dataset_row/dataset_compare both support exact-value filtering on a column. These boundaries are manageable but not perfectly crisp.
All tools share the dataset_ prefix and snake_case style, which is predictable and consistent. However, the second part mixes verbs (compare, search), nouns (columns, provenance, row), and adjectives/nouns (top, stats), so there is no uniform verb_noun pattern.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct type of data access or metadata need without feeling padded or redundant.
The surface covers schema, provenance, exact lookup, substring search, multi-value comparison, statistical summaries, and top/bottom ranking for the dataset. It lacks generalized arbitrary filtering or full row pagination, but the core read-only dataset workflow is well covered.