dataset_provenanceThe source, the date it was computed, the licence and the citation for the Yearendo 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 full burden. It implies an informational/read-only tool but does not explicitly state that it has no side effects, does not modify data, or makes no external calls. This is a minor gap.
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 concise, consisting of two short sentences that provide the essential information without redundancy or 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?
Given no parameters and no output schema, the description adequately covers what the tool returns and why it would be used. No additional context is necessary for correct invocation.
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 effectively 100% since there is nothing to document. The baseline for 0 params is 4, and the description adds no conflicting or missing parameter information.
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 the tool returns provenance metadata (source, computed date, licence, citation) for the Yearendo dataset. It is distinct from sibling tools that handle columns, rows, search, stats, and top, and the purpose is immediately understandable.
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 phrase 'Read this to attribute a figure correctly' gives a general usage hint, but it does not explicitly state when to choose this tool over siblings or when not to use it. No alternative tools are mentioned.
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, search, stats, provenance, and top/bottom comparisons are unambiguous. The main ambiguity is between dataset_row and dataset_compare, since both retrieve rows by exact column values, but descriptions clarify that compare handles multiple values in a specific order.
All tools follow a consistent dataset_ prefix with clear, lowercase snake_case names. The naming pattern is predictable and easy to scan, with no mixing of styles or vague generic verbs.
Seven tools is a well-scoped set for a single-dataset server. Each tool covers a distinct common operation—schema, lookup, search, comparison, stats, top values, and provenance—without unnecessary bloat.
The toolkit covers the core read-only operations needed for exploring and querying the Yearendo dataset: schema discovery, exact match, substring search, ordered comparison, numeric stats, ranking, and attribution. Minor gaps like grouped aggregations or combined filters exist, but agents can usually work around them with existing tools.