dataset_provenanceThe source, the date it was computed, the licence and the citation for the Reviewvo 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?
Since no annotations are provided, the description carries the behavioral burden, and it does disclose the exact metadata fields returned: source, computed date, licence, and citation. It omits output formatting details, but for a static, side-effect-free metadata lookup this is adequate.
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 definition is two short sentences with the metadata categories front-loaded and the actionable purpose at the end. No filler or redundancy.
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 both the content and the reason to call it. An agent has everything it needs to invoke 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 input schema has zero parameters, so there is no parameter semantic gap to compensate for. The description appropriately explains what metadata is returned instead of describing arguments.
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?
States a clear resource—the Reviewvo dataset provenance—and a specific purpose: correctly attributing a figure. The listed contents (source, computed date, licence, citation) make it semantically distinct from sibling tools that handle columns, rows, search, stats, and top-N data.
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?
Gives a clear trigger: read this when a figure needs attribution. It does not explicitly name alternatives or exclusions, but the sibling tools' purposes are sufficiently distinct that the intended use is unambiguous.
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 operation: schema, provenance, exact lookup, substring search, multi-value comparison, aggregate stats, and ranking. Minor overlap exists between dataset_row and dataset_compare (both match column values) and between dataset_columns and dataset_provenance (both expose provenance details), but the descriptions make the intended use clear.
All tools share the dataset_ prefix and use lowercase snake_case, giving a clear family identity. The suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), which is a minor inconsistency, but the overall pattern is predictable.
Seven tools is well-scoped for a single-dataset querying server. Each tool has a distinct role and none feels redundant or unnecessary for the core task of exploring and analyzing the Reviewvo dataset.
The surface covers schema discovery, provenance, exact/string lookup, multi-value comparison, numeric summaries, and ranking, which covers the main ways an agent would explore a dataset. Minor gaps include no pagination for large result sets and no multi-column filter, but these are workable given the available tools.