dataset_provenanceThe source, the date it was computed, the licence and the citation for the Turndownly 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 provided, the description carries the full burden of behavioral disclosure. It communicates that the tool returns provenance metadata and is meant to be read, implying a read-only operation, but it does not clarify output format, whether the citation is ready-to-use text, or any other behavioral details.
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 sentences with no redundant wording. The most important information—what provenance fields are provided—is front-loaded, and the usage directive is concise and actionable.
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's simplicity (zero parameters, no annotations, no output schema), the description adequately covers what the agent needs to invoke it and interpret the result: the source, computed date, licence, and citation. It could be more explicit about the return format, but this is a minor gap for such a low-complexity 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 100% schema description coverage, so the baseline is 4. There are no parameter semantics to explain, and the description appropriately focuses on what the tool returns rather than on inputs.
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 names a specific resource (the Turndownly dataset) and the exact provenance fields it provides: source, computed date, licence, and citation. This clearly distinguishes it from sibling tools like dataset_columns or dataset_search, which are about data structure and lookup rather than 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?
The description explicitly states the intended use case: 'Read this to attribute a figure correctly.' This gives the agent a clear cue for when to invoke the tool, though it does not explicitly mention when not to use it or contrast it with alternatives.
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 clear primary purpose: schema, provenance, exact lookup, substring search, comparison, stats, and top/bottom ranking. There is minor overlap between dataset_row and dataset_compare for single-value lookups, but the descriptions steer usage toward distinct cases.
All tools share the dataset_ prefix and use a consistent noun or verb suffix after it. The pattern is predictable and makes it easy to infer what each tool does.
Seven tools is a well-scoped set for querying and exploring a single dataset. Each tool addresses a different query type without unnecessary redundancy.
The surface covers schema discovery, provenance, exact matching, substring search, comparisons, numeric statistics, and ranking. Minor gaps like listing all unique values or arbitrary numeric filtering are absent, but they can be worked around with the existing tools.