dataset_provenanceThe source, the date it was computed, the licence and the citation for the Kbasevo 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 behavioral burden; 'Read this' signals a read-only retrieval, and the listed contents (source, computed date, licence, citation) disclose what the agent will get. It does not address output formatting or potential staleness, but that is minor 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 one compact sentence that front-loads the exact contents and follows with the purpose. No word is wasted and the title reinforces the message.
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 tool, the description fully covers what is returned and why an agent would use it. The provenance fields are enumerated and the attribution purpose is stated, so no critical guidance is missing.
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 the description need not explain parameters. The baseline of 4 applies.
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 and title clearly identify the tool as the provenance/metadata view for the Kbasevo dataset, naming the specific fields it covers (source, computed date, licence, citation). It is easily distinguished from sibling tools that operate on columns, rows, searches, and stats, though it lacks a direct operative verb such as 'retrieves'.
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?
'Read this to attribute a figure correctly' gives a clear use case for when an agent should call this tool. It does not explicitly enumerate when not to use it or compare it to sibling tools, but no alternative is really applicable for citation/provenance needs.
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.
The tools split cleanly into metadata (columns, provenance), retrieval (row, search, compare), and aggregation (stats, top). dataset_row and dataset_compare overlap somewhat since both filter by column values, but the multi-value ordered comparison purpose is distinct enough.
All tools share the dataset_ prefix and snake_case convention, making the family recognizable. However, the second half mixes noun-like names (columns, row, stats, top) with verb-like names (compare, search), so the pattern is consistent but not uniformly verb_noun.
Seven tools is a well-scoped set for exploring a single dataset: schema, provenance, exact lookup, substring search, comparisons, numeric stats, and extremes. No tool feels redundant, and the count is appropriate for the server's purpose.
The surface covers the common dataset questions: schema, attribution, exact matching, fuzzy search, comparative queries, numeric summaries, and ranking. It lacks advanced multi-condition filtering or full-dump pagination, but those are not clearly required for this read-only dataset browser.