dataset_provenanceThe source, the date it was computed, the licence and the citation for the Duesvo 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 burden of behavioral disclosure. It implies a read-only operation through 'Read this' and lists what the user will find, but it does not explicitly state that the tool has no side effects, whether it requires special access, or what format the provenance information takes. The description is adequate but leaves some behavioral details implicit.
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 short sentences convey the resource, the fields returned, and the intended use case with no filler or redundancy. The key content is front-loaded and every clause adds value.
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 simple zero-parameter provenance tool, the description is complete: it names the dataset, enumerates the returned attributes (source, date, licence, citation), and explains why the agent would invoke it. No output schema exists, but the listed fields provide sufficient expectation for the response.
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 coverage is 100%, so there is no parameter meaning to add. The description appropriately focuses on what the tool returns rather than parameter details, matching the baseline for param-free 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 resource (the Duesvo dataset) and the information returned (source, computed date, licence, citation). It lacks an explicit verb like 'returns' or 'gets', but 'The source...' plus 'Read this to attribute a figure correctly' makes the purpose unambiguous and distinguishes it from sibling dataset analysis tools.
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 context for when this tool should be used. No exclusions or alternative tool comparisons are provided, but for a zero-parameter provenance metadata tool, this level of guidance is adequate.
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 match, substring search, multi-value comparison, aggregate stats, and top/bottom ranking. The minor overlap between dataset_row, dataset_search, and dataset_compare could cause occasional misselection, but each description states its exact matching behavior.
All tools share the dataset_ prefix, which creates a strong family resemblance. However, the suffix is sometimes a noun (columns, provenance, row) and sometimes a verb (compare, search, stats, top), so the pattern is not fully uniform.
Seven tools is a well-scoped set for querying a single dataset. Each tool addresses a distinct common question type without redundancy or bloat.
The set covers schema discovery, provenance, exact lookups, fuzzy search, controlled comparison, numeric aggregates, and sorted extremes. A general arbitrary filter or grouped analysis is missing, but the provided tools handle the most likely dataset questions.