dataset_provenanceThe source, the date it was computed, the licence and the citation for the Mashzo 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 clearly lists the information returned (source, date, licence, citation), implying a read-only, informational operation. It doesn't mention any side effects or permissions, which is appropriate for a metadata retrieval tool. The description is transparent enough for an agent to know what to expect.
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 concise sentences with no filler. The first sentence lists the content, and the second explains the use case. Every word earns its place, and the purpose is front-loaded.
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 zero-parameter tool with no output schema, the description fully explains what the tool returns and why to use it. It could mention the format of the citation or whether it returns a single object, but that's not essential for an agent to call it correctly. The description is sufficiently complete for the tool's simplicity.
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 the schema is trivially complete. The description doesn't need to explain any parameter semantics because none exist. The baseline of 4 is appropriate given the absence of parameters.
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: the source, computed date, licence, and citation for the Mashzo dataset. It uses specific nouns and clearly differentiates from sibling tools like dataset_columns, dataset_search, and dataset_stats by focusing on provenance rather than data manipulation.
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 use case. While it doesn't explicitly state when not to use it or mention alternatives, the purpose is so specific that the context is obvious. It would benefit from an explicit 'use this when you need citation information' but the implicit guidance is strong.
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.
The tools are largely distinct: schema, provenance, exact lookup, substring search, compare, stats, and top-k. The only mild overlap is among dataset_row, dataset_search, and dataset_compare, since they all retrieve rows, but their descriptions clarify exact match, contains, and value-list comparison respectively.
Every tool uses a consistent `dataset_` prefix with clear snake_case names. Even though some suffixes are nouns and some are verbs, the pattern is uniform and predictable across the entire tool set.
Seven tools is a well-scoped size for a dataset querying server. Each tool covers a distinct common operation without feeling redundant or excessive.
The surface covers the core dataset operations well: schema inspection, provenance, exact lookup, search, comparisons, statistics, and top/bottom ranking. Minor gaps include distinct-value enumeration and grouped aggregation, but these are not fatal for typical dataset questions.