dataset_provenanceThe source, the date it was computed, the licence and the citation for the PerDiemDesk 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 provided, the description carries the full burden. It states exactly what information will be returned (source, computed date, license, citation), which is the core behavioral trait. It does not explicitly state it is read-only or side-effect-free, but the nature of the tool makes that clear. The description adds value beyond a simple 'get provenance' by enumerating the 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?
Two short sentences with no redundant wording. The core information is front-loaded in the first sentence, and the usage directive is concise and actionable. 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 simple, parameterless metadata lookup tool with no output schema, the description is fully sufficient. It tells the agent what the tool returns and when to use it. No additional context is needed for correct invocation or interpretation.
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, and the schema coverage is trivially 100%. The description does not need to explain any parameters. The baseline for 0-parameter tools is 4, and the description fulfills that by not attempting to describe nonexistent parameters.
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 identifies the tool's purpose: it returns provenance metadata (source, computed date, license, citation) for the PerDiemDesk dataset. It is distinct from sibling tools which focus on columns, comparison, rows, search, stats, and top values, so an agent can easily differentiate.
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?
It provides a clear usage context: 'Read this to attribute a figure correctly.' This implies the tool should be used when attribution is needed. It doesn't explicitly list exclusions or alternative tools, but the distinct purpose makes it obvious that it is not for data manipulation or queries.
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 targets a distinct query pattern—schema, provenance, exact rows, substring search, stats, top values, and multi-value comparisons—so an agent can generally choose correctly. Some overlap exists between dataset_row and dataset_compare (both filter rows by column values), and dataset_search overlaps with dataset_row on substring matches, but the descriptions are clear enough to resolve the ambiguity.
All tool names follow the same dataset_<operation> pattern, with clear nouns like columns, row, search, stats, top, compare, and provenance. The naming is uniform and predictable, with no mixed conventions.
Seven tools is a well-scoped count for a single-dataset exploration server. Each tool covers a distinct query need without redundancy or unnecessary bloat.
The tool surface fully covers the read-only dataset workflow: schema inspection, provenance, exact lookup, substring search, statistical summaries, extreme-value ranking, and side-by-side comparisons. No obvious gaps would prevent an agent from answering typical questions about this dataset.