dataset_provenanceThe source, the date it was computed, the licence and the citation for the LPreportly 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 full burden of disclosing behavior. It clearly states this is a read-only tool returning provenance metadata (source, date, licence, citation), which is enough to infer there are no side effects. It could mention return formatting, but that is not necessary for such a simple tool.
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 with no filler. The most useful information (what the tool returns) is front-loaded, and the intended use case is stated crisply.
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 parameterless, read-only metadata utility, the description covers everything an agent needs: the content of the data, its purpose, and when to consult it. The absence of an output schema is not a gap because the description enumerates the fields returned.
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 to document. The description adds no parameter information, but none is needed; a baseline of 4 is appropriate when parameters are absent.
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 resource (dataset provenance) and the specific facts it provides: source, computation date, licence, and citation. It also frames its purpose as helping the user attribute a figure correctly, which distinguishes it from sibling tools like dataset_columns or dataset_stats.
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 tells the user when to use it: 'Read this to attribute a figure correctly.' It gives a clear use case, though it does not enumerate when not to use it or compare it to alternatives. For a simple metadata tool, this is sufficient guidance.
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.
Most tools have clearly distinct purposes: schema, provenance, exact row lookup, text search, multi-value comparison, stats, and top-N. dataset_row and dataset_compare could be confused since both filter on column values, but compare explicitly handles multiple given values and ordering.
All tools share a consistent dataset_ prefix and use lowercase snake_case, making the pattern predictable. However, the second part mixes noun-style names (columns, provenance, row) with verb/action-style names (compare, search, stats, top), so it is not a uniform verb_noun convention.
Seven tools is a well-scoped size for a single-dataset read-only server. Each tool provides a distinct mode of access or summary without unnecessary redundancy or bloat.
The set covers schema discovery, provenance, exact-value lookup, substring search, multi-value comparison, numeric statistics, and top/bottom rows. Minor gaps exist, such as no pagination or distinct-value listing, but most dataset exploration questions can be answered with the available tools.