dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Yieldzo 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?
The description enumerates the exact output contents (columns, numeric indicators, row count, provenance banner), which sets clear expectations. It does not explicitly state that the operation is read-only or non-destructive, but the nature of returning schema metadata makes that implicit. Slight deduction for not mentioning potential errors or side effects, though none are expected.
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 extremely concise—two sentences—while conveying the purpose, the output contents, and usage priority. Every word adds value, with no redundancy or filler.
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?
Given there is no output schema, the description fully specifies what the tool returns and when to use it. It is sufficiently complete for an agent to know the tool's role within the sibling toolset and act accordingly.
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 no parameters, so the input schema is fully covered by an empty properties object. The description adds no parameter details because none exist. Per the baseline for high schema coverage, a score of 3 is appropriate.
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 returns columns, numeric flags, row count, and a provenance banner for the Yieldzo dataset. It also explicitly identifies this as the first call to learn the schema, distinguishing it from sibling tools like dataset_compare or dataset_search.
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 gives direct usage guidance: 'Call this first to learn the schema.' This tells the agent exactly when to invoke this tool, making the usage context unambiguous.
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 has a clearly distinct purpose: schema, row matching, provenance, exact lookup, substring search, aggregation, and extreme values. No overlap or ambiguity.
All tools follow a consistent 'dataset_<action>' pattern (columns, compare, provenance, row, search, stats, top), making the naming predictable and clean.
With 7 tools, the set is well-scoped for a dataset exploration server—enough to cover core operations without being bloated or sparse.
The toolkit covers schema inspection, data retrieval (exact, substring, multi-value comparison), statistics, top/bottom values, and provenance. Missing only niche operations like distinct value enumeration or sampling, but nothing critical for typical dataset exploration.