dataset_provenanceThe source, the date it was computed, the licence and the citation for the Hreflangly 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 explicitly enumerates the kinds of information returned: source, computed date, licence, and citation. This is sufficient for a zero-parameter, read-only metadata tool; no destructive or state-changing behavior is implied.
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 compact sentences with no filler. The first sentence lists the exact contents, and the second gives the practical use case, making every word useful.
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 low-complexity metadata tool with no input schema parameters and no output schema, the description is complete. It tells the agent what the tool provides, why to call it, and what information will be available for citation.
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 no parameter semantics burden on the description. The baseline of 4 applies because there is nothing to document beyond the tool's fixed output.
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 tool provides provenance metadata — source, computed date, licence, and citation — for the Hreflangly dataset. It is distinct from sibling tools like dataset_columns or dataset_row because it addresses attribution rather than data manipulation or querying, although it does not explicitly name a sibling.
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 instruction 'Read this to attribute a figure correctly' tells the agent when to use the tool: when citation or attribution is needed. It does not explicitly describe when not to use it or name alternatives, so it falls just short of full usage 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 lookup, substring search, stats, top values, and multi-value comparison. The only mild ambiguity is between dataset_row and dataset_compare, since both return rows filtered by column values, and between dataset_columns and dataset_provenance, since both mention provenance.
All tool names consistently use the dataset_ prefix and snake_case, which makes the set feel cohesive. However, the suffix style is mixed: some are nouns like columns, provenance, row, and stats, while others are verbs like compare and search.
Seven tools is well within the ideal range for a dataset-focused server. Each tool covers a distinct analytical need without redundancy, so the count feels intentional and appropriately scoped.
The toolset covers schema discovery, provenance, exact row lookup, free-text search, targeted comparison, numeric statistics, and extreme-value ranking. For a read-only dataset querying server, this is a complete and practical surface with no obvious dead ends.