dataset_provenanceThe source, the date it was computed, the licence and the citation for the Opexvo 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 itself signals a read-only operation ('Read this') and enumerates exactly what will be returned: source, computed date, license, and citation. It could be more explicit about having no side effects, but the described behavior is adequately transparent for a zero-parameter metadata tool.
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 with no filler. It front-loads the concrete content items and ends with a clear instruction on when to use it.
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, no-output-schema provenance lookup, the description is complete: it states what data is returned, the dataset it applies to, and the intended use. 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, so there is no schema information to supplement. The description's enumeration of the returned provenance fields provides all the semantic context an agent needs.
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 role: it exposes provenance metadata—source, computed date, license, and citation—for the Opexvo dataset. This is distinctive from the sibling tools, which handle columns, search, stats, and rows rather than provenance or attribution.
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 clear, concrete use case: citation and attribution. It does not explicitly list alternatives or exclusion conditions, but for a simple metadata retrieval tool this context is sufficient.
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 primary purpose: schema, provenance, exact row lookup, substring search, multi-value comparison, summary stats, and top/bottom rankings. The main ambiguity is between dataset_row and dataset_compare, since both do exact value filtering, though one is single-value and the other is multi-value/ordered.
All tools share a clean dataset_ prefix and use snake_case, making the family immediately recognizable. The second part mixes noun forms (columns, provenance, row, stats) with verb-like forms (compare, search, top), so the pattern is not perfectly uniform but remains readable and predictable.
Seven tools is a well-scoped size for a read-only dataset exploration server. Each tool covers a distinct need without redundancy or unnecessary bloat.
The tool surface covers the full read-only dataset workflow: schema discovery, row retrieval by exact match, substring search, multi-value comparison, numeric summaries, ranking, and provenance/attribution. There are no obvious missing operations for the stated purpose.