dataset_provenanceThe source, the date it was computed, the licence and the citation for the Sopvo 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?
No annotations are provided, so the description carries the full burden. It clearly states the tool returns provenance information (source, date, licence, citation) with no side effects implied. The zero-parameter schema is consistent with a simple metadata retrieval.
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 and well-structured, using a clear list of the data elements (source, date, licence, citation) and a directive to read it for attribution. Every sentence adds value.
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 the tool's simple purpose and no parameters or output schema, the description covers the essential context: what information is provided and why an agent would use it. It could mention that the output is informational and read-only, but that is implicit in the zero-parameter design.
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 no parameters, and the schema reflects that. The description does not need to explain parameters since there are none, and the baseline of 4 is appropriate for a parameterless tool.
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's purpose: to provide data provenance including source, computation date, licence, and citation. It distinguishes itself from sibling tools that handle columns, comparison, rows, search, statistics, and top values.
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 says to read this 'to attribute a figure correctly', which gives clear guidance on when to use it. It does not explicitly mention when not to use it, but the sibling tools are distinct enough that no exclusion is needed.
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 on the Sopvo dataset: schema (columns), metadata (provenance), exact row retrieval (row), substring search (search), statistics (stats), ranking (top), and value comparison (compare). No two tools overlap in purpose, making selection unambiguous.
All tools follow a consistent 'dataset_<operation>' pattern with lowercase snake_case, such as dataset_columns, dataset_search, and dataset_stats. The naming is uniform and predictable, aiding agent selection.
With 7 tools, the server is well-scoped for exploring a single dataset. Each tool covers a necessary aspect—schema, provenance, data access, search, stats, and top/bottom queries—without bloat or missing essentials.
The tool surface comprehensively covers the domain of dataset exploration: schema discovery, metadata, exact and fuzzy retrieval, comparison, statistical summaries, and extreme-value queries. No obvious gaps exist for a read-only dataset server.