dataset_provenanceThe source, the date it was computed, the licence and the citation for the Monthendly 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 and does a good job: it signals a read-only metadata lookup and enumerates the returned fields. It does not discuss error cases or permissions, but for a zero-parameter provenance lookup that 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?
A single compact sentence that front-loads the content of the tool and ends with the reason to use it. Every word earns its place; no filler or repetition of the title.
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 tool with no output schema, the description fully covers the returned information and the intended use case. An agent can correctly invoke this tool and know what to expect without needing any additional context.
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 schema coverage is 100%, so there is nothing for the description to add about parameters. The baseline of 4 applies because no parameter documentation is needed at all.
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, computed date, licence, and citation for the Monthendly dataset. It is clearly distinct from sibling tools like dataset_row, dataset_stats, or dataset_search, which handle data access rather than provenance metadata.
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 explicitly tells the agent when to use it: 'Read this to attribute a figure correctly.' It does not explicitly list when-not-to-use conditions or alternatives, but the sibling tool names make the boundary obvious, and the guidance is actionable.
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 has a distinct purpose (schema, provenance, exact match, substring search, multi-value compare, stats, top/bottom), and descriptions clearly differentiate them. The only mild overlap is between dataset_row and dataset_compare, but the exact-match vs multi-value distinction is explicit enough to avoid serious confusion.
All tools share a consistent 'dataset_' prefix in snake_case, which creates a clear family identity. However, suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), so the pattern is not perfectly uniform verb_noun.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct query pattern without redundancy or bloat, and the count sits comfortably within the ideal 3–15 range.
The tool surface covers the core needs for working with a dataset: schema discovery, provenance, exact lookup, full-text search, comparisons, numeric aggregates, and top/bottom ordering. Minor gaps exist, such as no dedicated count-by-filter or multi-column filtering, but agents can work around these using existing tools.