dataset_provenanceThe source, the date it was computed, the licence and the citation for the Upkeepvo 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 disclosure burden. It lists the type of information returned, which is useful, but it does not disclose response format, error behavior, or whether the data is always available for any dataset.
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?
Two sentences with no filler. The first sentence names the delivered content, and the second states the concrete usage intent. Every word earns its place.
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 lookup, the description is nearly complete: it states what is returned and why to call it. The only gap is the lack of an output schema or a note on return shape, but this is minor for a simple provenance query.
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 parameter semantics to document. The baseline of 4 applies because nothing is missing and the schema requires no clarification.
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 as providing source, computation date, licence, and citation for the Upkeepvo dataset. It is distinct from siblings like dataset_columns or dataset_row, though it lacks an explicit verb like 'retrieve'.
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' tells the agent when to use this tool. It does not explicitly contrast it with alternatives, but no sibling serves the same provenance purpose.
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 discovery, comparison, provenance, exact row lookup, substring search, stats, and ranking. No two tools overlap in functionality, making misselection unlikely.
All tools follow the consistent pattern 'dataset_' followed by a lowercase noun or verb (columns, compare, provenance, row, search, stats, top). The naming is uniform and predictable.
With 7 tools, the set is well-scoped for a dataset querying server. Each tool covers a distinct query need without redundancy or bloat.
The tools provide comprehensive read-only access to the dataset: schema, row retrieval, search, statistics, ranking, comparisons, and provenance. No obvious gaps for typical analysis workflows.