dataset_provenanceThe source, the date it was computed, the licence and the citation for the Yieldzo 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?
There are no annotations, so the description carries the full burden. It implies a read-only metadata lookup with 'Read this to attribute a figure correctly', but it does not explicitly state that there are no side effects, or describe error or availability behavior.
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 a single, compact sentence that conveys the content and use case without unnecessary detail or repetition.
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?
The description is sufficient for a simple metadata retrieval tool: it names the exact pieces of information returned and the intended purpose. It does not describe an output format, but no output schema exists and the scope is narrow.
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, so the baseline is 4. The description does not need to explain parameter meanings because there are none.
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 that the tool returns provenance metadata (source, date computed, licence, citation) for the Yieldzo dataset and that it is meant for attribution. It lacks a strong imperative verb, but the purpose is unmistakable.
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 gives a use case ('attribute a figure correctly') but does not explicitly contrast with sibling dataset tools or mention when this tool should be preferred over alternatives. The guidance is implicit rather than explicit.
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 clearly distinct purpose: schema, row matching, provenance, exact lookup, substring search, aggregation, and extreme values. No overlap or ambiguity.
All tools follow a consistent 'dataset_<action>' pattern (columns, compare, provenance, row, search, stats, top), making the naming predictable and clean.
With 7 tools, the set is well-scoped for a dataset exploration server—enough to cover core operations without being bloated or sparse.
The toolkit covers schema inspection, data retrieval (exact, substring, multi-value comparison), statistics, top/bottom values, and provenance. Missing only niche operations like distinct value enumeration or sampling, but nothing critical for typical dataset exploration.