dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Soapvo dataset. Call this first to learn the schema.
| 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 must carry the full behavioral disclosure burden. It lists the returned elements (columns, numeric flags, row count, provenance banner), which is useful, but it does not explicitly state that the tool is read-only or describe any side effects, limitations, or response format. This is a moderate gap for a tool that is clearly a schema inspection.
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 wasted words. The first sentence front-loads the core content (what it returns) and the second gives a clear usage directive. It is concise, structured, and every part earns its place.
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 sufficiently conveys what the agent can expect: a set of columns, numeric flags, row count, and provenance banner. It lacks explicit format details (e.g., whether the response is an object or array), but for a schema discovery tool, this is adequate and covers the essential information needed to call it correctly.
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 description coverage is 100% (empty schema), so the description does not need to add parameter-level meaning. According to the rubric, a zero-parameter tool gets a baseline score of 4, and the description provides no extraneous parameter info.
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 what the tool returns: the columns, their numeric status, row count, and provenance banner. It also identifies the specific dataset (Soapvo) and instructs to call it first to learn the schema, which distinguishes it from sibling tools that perform other operations.
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 'Call this first to learn the schema' provides explicit when-to-use guidance, positioning it as the entry point for schema discovery. It does not explicitly list alternatives, but the context strongly implies it is the initial step before using other tools like dataset_stats or dataset_row.
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.