dataset_provenanceThe source, the date it was computed, the licence and the citation for the Attestroom 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 describes the content of the tool's response: source, computation date, licence, and citation. This is a simple read-only metadata lookup with no destructive or state-changing behavior implied, and the description makes that nature sufficiently 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?
The description is two short sentences that front-load the key content fields and end with the practical use case. Every word earns its place, and there is no redundant or vague filler.
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 provenance lookup, the description is nearly complete: it names the dataset, the fields the user will receive, and the intended usage. There is no output schema, so a slightly more explicit statement of the exact return format would be a minor improvement, but it is not essential for correct tool selection.
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 there is nothing the description needs to explain about parameter semantics. The description appropriately focuses on what the tool returns rather than how to configure it.
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 what the tool provides: the source, computation date, licence, and citation for the Attestroom dataset. It is distinct from the sibling tools, which focus on columns, rows, stats, and comparisons, whereas this tool is about provenance and attribution. It lacks an explicit action verb like 'get' or 'returns,' but the intent is unambiguous.
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 sentence 'Read this to attribute a figure correctly' gives a clear condition for when to use the tool: whenever the user needs to cite or attribute the dataset. It does not explicitly enumerate when not to use it, but the sibling tool names and the narrow provenance scope make the appropriate use case 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, stats, and top/bottom queries. dataset_row and dataset_compare overlap somewhat since both filter on column values, but the descriptions clarify exact single-value matching versus ordered multi-value comparison.
All tools consistently share the dataset_ prefix and use lowercase snake_case, which makes the set feel unified. However, suffixes are a mix of nouns (columns, provenance, row, stats) and verbs (compare, search), so the pattern is not perfectly uniform.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a distinct common operation without feeling padded or redundant.
The set covers the essential dataset operations: schema discovery, provenance, exact match, text search, numeric statistics, ranking, and multi-value comparison. Minor gaps exist such as pagination for search results and range-based numeric filters, but agents can generally answer common questions without dead ends.