dataset_provenanceThe source, the date it was computed, the licence and the citation for the Xlifflane 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 burden of behavioral disclosure. It does reveal what content is returned (source, date, licence, citation), but it does not state whether the operation is read-only, what the output structure looks like, or whether any network or licensing checks occur. For a simple metadata getter this is acceptable 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?
Two short sentences, no filler. The content list is front-loaded and the usage instruction is placed at the end, making it easy to parse quickly.
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?
Given the tool has no parameters and no output schema, the description covers the essential information an agent needs: what data it returns and when to use it. It could mention output format or that no arguments are required, but those are minor gaps for such a simple tool.
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 and the input schema is empty with 100% coverage, so there is nothing for the description to add. The baseline for zero-parameter tools is 4, and the description adequately implies no inputs are needed.
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 provides: source, computation date, licence, and citation for the Xlifflane dataset. It clearly separates this from sibling data-manipulation tools like dataset_row or dataset_stats by focusing on provenance and attribution.
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?
'Read this to attribute a figure correctly' gives an explicit use case for when to call this tool. It does not mention exclusions or alternatives, but among the siblings only this tool concerns provenance, so the context is clear enough.
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, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/lowest rows. The main potential confusion is between dataset_row and dataset_compare, since both handle exact value matching, but the multi-value ordering intent of dataset_compare keeps them separable.
All tools share a consistent dataset_ prefix followed by a clear operation or noun: columns, compare, provenance, row, search, stats, top. The naming pattern is uniform and predictable.
Seven tools is a well-scoped set for querying and exploring a single dataset. Each tool covers a distinct need without redundancy, and the count feels neither sparse nor bloated.
The set covers schema inspection, provenance, exact lookups, substring search, multi-value comparisons, numeric statistics, and top/bottom ranking. A minor gap is the lack of a tool to retrieve all rows or page through large result sets, but the existing tools are sufficient for most dataset questions.