dataset_provenanceThe source, the date it was computed, the licence and the citation for the Dsarvo 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 carries the full burden of behavioral disclosure. It indicates this is a read-only informational tool and lists the fields returned, which is helpful. However, it does not explicitly state that no side effects occur, describe the response format, or mention any access considerations.
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 sentences long, front-loads the core content areas, and ends with a clear usage directive. Every sentence contributes meaning; there is no redundant or filler text. The minor typo 'Dsarvo' does not undermine overall conciseness.
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 provenance/metadata tool, the description covers the main content—source, date, licence, and citation—and the intended use. With no output schema, a bit more detail about the return format or citation format could be added, but the tool is simple enough that the current description is largely complete.
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 baseline of 4 applies. There is no parameter information needed in the description, and the description appropriately focuses on the tool's purpose and output.
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 what the tool provides: source, computation date, licence, and citation for the Dsarvo dataset. It is distinguishable from sibling tools like dataset_columns or dataset_search because it is about provenance and attribution, not data access or analysis. It lacks an explicit operational verb like 'returns' or 'fetches', but the intent is unambiguous.
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 use case for when to invoke this tool. It does not explicitly name alternative tools or provide exclusion criteria, but the provenance-focused context is sufficient to guide selection among the dataset_* siblings.
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 metadata and numeric tools (columns, provenance, stats, top) are clearly distinct, but dataset_row, dataset_search, and dataset_compare all retrieve rows by value, so an agent could initially confuse exact-match, contains-search, and multi-value ordering. The descriptions do clarify the boundaries, making the overlap manageable.
All tools share the consistent dataset_ prefix and lowercase style, but the second part mixes nouns (row, columns, provenance, stats) with verbs (compare, search, top). This is predictable enough, though not a strict verb_noun pattern.
Seven tools is well-scoped for a single-dataset server: schema discovery, provenance, three retrieval modes, statistics, and ordering each earn their place without bloat or thinness.
The tool surface covers the main workflows: learn the schema, attribute correctly, look up exact/contains/multi-value rows, compute numeric summaries, and find top/low values. No obvious dead-end operations are missing for the apparent purpose.