dataset_provenanceThe source, the date it was computed, the licence and the citation for the Soapvo 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 implies a read operation via 'Read this', but does not state whether it is side-effect-free, the format of the returned data, or any potential variability (e.g., if the dataset is updated over time). It discloses what is returned but not much else.
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 the core content list front-loaded and the use case immediately after. Every word earns its place; there is no fluff or redundancy.
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 tool with no parameters, no output schema, and a simple metadata retrieval purpose, the description fully covers what the tool does and when to use it. An agent can correctly decide to invoke this tool for attribution without needing additional information.
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 empty. The baseline for 0 parameters is 4. The description adds meaningful context about the tool's output (source, date, licence, citation), which is valuable beyond the empty schema. It does not need to explain any parameters because there are none.
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, computation date, licence, citation) for the Soapvo dataset, and explicitly ties it to the task of correctly attributing a figure. This distinguishes it from sibling tools like dataset_row or dataset_stats, which deal with data content rather than metadata.
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 includes a direct use-case directive ('Read this to attribute a figure correctly'), which tells an agent when to invoke the tool. It does not explicitly state when not to use it or mention alternatives, but given the narrow scope and the sibling names (dataset_columns, dataset_search, etc.), the context is clear enough.
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 operation type (schema, provenance, exact lookup, substring search, multi-value compare, stats, top/bottom), so boundaries are mostly clear. dataset_compare is slightly vague by name but its description distinguishes it from dataset_row and dataset_search.
All tools follow a predictable `dataset_<topic>` snake_case pattern. Even though some suffixes are nouns and some are verbs, the uniform prefix and lowercase underscore style make the set feel consistent.
Seven tools is a well-scoped size for a single-dataset exploration server. Each tool serves a clear querying or metadata need without redundancy.
The toolkit covers schema, provenance, exact match, search, multi-value comparison, numeric stats, and top/bottom rows—a broad and practical surface. Obvious missing pieces are distinct-value enumeration and group-by aggregates, but most common questions can be answered with the existing tools.