dataset_provenanceThe source, the date it was computed, the licence and the citation for the Longtailo 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 burden of behavioral disclosure. It states what data is returned but does not mention side effects, errors, or authentication requirements. For a simple metadata lookup with zero parameters, the implied read-only behavior is reasonably clear, but not fully detailed.
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 redundancy. The substantive content is front-loaded, and the practical purpose is stated in a concise second sentence.
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 metadata tool with no output schema, the description adequately lists the returned fields (source, date, licence, citation) and explains why to use it. It omits details about output formatting, but that is a minor gap for such a straightforward 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 schema covers 100%, so no parameter explanation is needed. The description appropriately focuses on the returned fields rather than arguments, matching the baseline for parameterless tools.
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 returning provenance metadata—source, computed date, licence, and citation—for the Longtailo dataset. This distinguishes it from the other dataset_* sibling tools, though it lacks an explicit verb like 'returns' or 'retrieves'.
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 phrase 'Read this to attribute a figure correctly' gives a clear, specific use case for when to invoke the tool. It does not explicitly exclude alternatives, but the sibling tools are plainly unrelated to provenance or citation.
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 targets a distinct query mode—schema, provenance, exact match, search, comparison, stats, and top-N—so there is little real ambiguity. The main possible confusion is between dataset_row and dataset_compare, since both filter by column values, but the descriptions clarify that compare is for ordered multi-value lookups while row handles single exact-value matches.
All tool names follow a consistent dataset_<noun> pattern with lowercase snake_case, making the family easy to recognize and predict. Though the names use nouns rather than verbs, the convention is uniform and clear.
Seven tools is a well-scoped set for querying and analyzing a single dataset. Each tool covers a distinct data-access need without redundancy or unnecessary bloat.
The surface covers schema discovery, provenance, exact and fuzzy lookup, multi-value comparisons, numeric stats, and top/bottom selection, which is comprehensive for typical dataset questions. A minor gap is the lack of categorical frequency counts or a way to retrieve all rows without a filter, but agents can work around those by combining existing tools.