dataset_provenanceThe source, the date it was computed, the licence and the citation for the BurdenRateLedger 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 burden. It discloses exactly what information will be returned — source, computed date, licence, and citation — and frames the tool as read-oriented ('Read this'). It does not explicitly state side-effect-freedom, but for a metadata lookup tool the disclosure is fairly complete.
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 short, efficient sentences with no filler. It front-loads the key content elements and ends with a direct usage instruction, making it easy for an agent 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?
For a simple parameterless provenance lookup, the description is complete: it names the dataset, lists the provenance fields, and gives the intended use case (attribution). No output schema exists, but the description sufficiently explains what the agent will get.
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, and the schema coverage is 100%, so there are no parameter semantics to clarify. The description adds useful context by naming the specific dataset, but this is a baseline-4 situation for a parameterless tool.
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 identifies the tool as providing provenance metadata for the BurdenRateLedger dataset: source, computation date, licence, and citation. It names a specific resource and an actionable verb (attribute), and it is easy to distinguish from sibling data-manipulation tools like dataset_columns 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 states when to use this tool: when a figure needs to be attributed correctly. It does not explicitly list when not to use it or name alternatives, but the use case is concrete and the sibling tools are clearly different in purpose.
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, search, stats, top, and comparison. dataset_compare and dataset_row both filter by column values but are differentiated by multi-value ordering versus single exact match, so there is minor potential overlap but descriptions clarify it.
All tools follow the same dataset_<noun> pattern with snake_case naming. The verbs are semantically clear and consistent across the set, making the tool surface predictable.
Seven tools is well-scoped for a single-dataset analysis server. Each tool covers a distinct query need without redundant or excessive surface area.
The tool set covers schema inspection, provenance, exact and fuzzy lookup, comparisons, summary statistics, and top/bottom ranking. It lacks more advanced analytical operations like grouping or arbitrary aggregation, but for the stated dataset-focused purpose it provides solid coverage.