dataset_provenanceThe source, the date it was computed, the licence and the citation for the RoughInDesk 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 behavioral disclosure burden. It implies a read-only informational operation and lists the content returned, but it does not explicitly state that no data is modified or describe any side effects. This is adequate but not fully transparent.
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 and front-loads the core content (source, date, licence, citation) before the usage instruction. Every part adds value, and the title reinforces the purpose without redundancy.
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, informational provenance tool, the description is complete: it names the dataset, the key metadata fields, and the intended use case. No additional context is necessary for an agent to invoke it correctly.
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 nothing for the description to clarify about inputs. The schema is fully covered by being empty, and the description appropriately focuses on what the tool returns rather than parameters.
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 provides provenance metadata—source, computed date, licence, and citation—for the RoughInDesk dataset. It also names the intended action ('attribute a figure correctly'), making its purpose distinct from sibling tools like dataset_row, dataset_stats, or dataset_search.
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 identifies when to use the tool: when attribution is needed. It does not enumerate alternatives or exclusions, but the sibling tool names make the differentiation clear, and the use case is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Tools are mostly distinct, with clear descriptions for schema, provenance, stats, and top-N queries. Some overlap exists among row retrieval tools (dataset_row, dataset_compare, dataset_search), but descriptions clarify exact match, multi-value match, and substring search.
All tools share the consistent 'dataset_' prefix, but the second part mixes nouns (columns, provenance, row, stats), verbs (compare, search), and an adjective (top), so the naming pattern is not uniform.
Seven tools is well-scoped for a read-only dataset exploration server, covering schema, metadata, lookup, search, comparison, statistics, and top/bottom queries without unnecessary bloat.
The tool set covers the full range of expected dataset operations: schema inspection, provenance, exact row retrieval, substring search, multi-value comparison, numeric stats, and ranking. No obvious gaps for read-only dataset analysis.