dataset_provenanceThe source, the date it was computed, the licence and the citation for the Eobify 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 full burden. It discloses the returned content (source, computed date, licence, citation) and 'Read this' implies a read-only operation, but it never explicitly states that there are no side effects or what the actual citation output looks like.
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 short sentences, front-loaded with the substantive field list and immediately followed by the practical use case. No filler, no redundancy, and no repetition of the tool name.
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, zero-output-schema metadata tool, the description covers what data is returned and when to use it. It does not detail the exact citation format or return envelope, but that is a minor gap given the tool's simplicity.
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 input schema has zero properties and schema description coverage is 100%, so there are no parameters to document. The description does not need to compensate for any parameter documentation gap.
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 states exactly what the tool exposes: the source, computed date, licence, and citation for the Eobify dataset. It clearly acts as a provenance/metadata accessor and is distinct from sibling tools that deal with rows, columns, stats, or 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 gives a concrete trigger: read this when attributing a figure correctly. It implies the use context and makes clear the tool is for citation/provenance rather than data interrogation, though it does not explicitly name alternative tools or exclusions.
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 defined purpose, but dataset_row and dataset_compare overlap conceptually since both retrieve rows by column value, just with different cardinality and ordering. The other tools are clearly separated between schema, provenance, search, statistics, and top/bottom ranking.
All tool names consistently use the dataset_ prefix followed by a concise operation name in snake_case. The pattern is uniform and predictable, making it easy to infer what each tool does.
Seven tools is a well-scoped set for a single-dataset server. Each tool covers a distinct common query type, and none feel redundant or unnecessary.
The tool surface covers the main dataset exploration needs: schema, provenance, exact lookup, substring search, multi-value comparison, numeric statistics, and top/bottom rows. Minor gaps exist, such as no direct count of filtered rows or grouped aggregation, but agents can work around these with existing tools.