dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the DamageRestore HQ 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?
No annotations are provided, so the description carries the full burden. It discloses the exact contents of the response (columns, numeric indicators, row count, provenance banner), which is sufficient for a simple read-only tool with no parameters. It does not describe side effects, but none are expected for a schema inspection tool.
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 sentences with no fluff. The first sentence front-loads the output contents, and the second gives the usage directive. Every word 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 tool with no parameters and no output schema, the description fully covers what an agent needs: what it returns and when to call it. Nothing is missing for correct invocation.
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 nothing to document. Per the rubric, a baseline of 4 is appropriate when there are no parameters, and the description correctly omits parameter details since none exist.
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 provenance banner for a specific dataset. It also positions itself as the schema discovery tool, differentiating it from sibling tools like dataset_stats or dataset_row which serve different purposes.
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?
Explicitly instructs to call this tool first to learn the schema, giving clear temporal guidance for when to use it. This establishes it as the entry point for exploring the dataset, which is a strong usage directive.
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: schema inspection, row filtering, comparison, provenance, exact match, search, statistics, and top/bottom rows. No two tools overlap in purpose.
All tools follow a consistent 'dataset_' prefix with clear noun/verb suffixes (columns, compare, provenance, row, search, stats, top). Pattern is uniform and predictable.
Seven tools cover the core dataset exploration operations without bloat. Each earns its place for a data querying server.
The set covers schema, row retrieval, search, comparison, statistical summaries, provenance, and top/bottom queries—complete for read-only data exploration. No gaps for typical dataset questions.