dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Rebadgo 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 carries the burden, and it discloses what the call returns: columns, numeric status, row count, and provenance banner. It implies a read-only schema-introspection behavior. It could more explicitly state that no dataset transformation occurs, but for a zero-parameter informational tool this is sufficient.
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?
A single sentence that front-loads the concrete outputs and places the actionable guidance ('Call this first') at the end. Every phrase earns its place; there is no 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?
For a zero-parameter tool with no output schema and no annotations, the description is complete: it enumerates the return contents and gives workflow ordering. An agent has everything it needs to decide to call this tool first.
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 baseline is 4. The schema is an empty object with 100% coverage, and the description correctly avoids inventing parameter details.
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 uses specific verbs and resources: it tells the agent what this tool returns (columns, numeric flags, row count, provenance banner) and frames it as the schema-learning entry point. It is clear, though it does not explicitly name or differentiate from sibling 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?
It gives explicit context: 'Call this first to learn the schema,' which tells an agent when this tool should be used in the workflow. It does not mention exclusions or alternatives, but the placement guidance is genuinely useful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool serves a clearly distinct purpose: schema exploration, provenance, exact row lookup, substring search, aggregate stats, top/bottom rows, and value comparisons. Even the similar-looking row and search tools differ in exact match vs. substring match, so agents can reliably choose the right one.
All tools share the consistent prefix 'dataset_' followed by a single, descriptive word (columns, compare, provenance, row, search, stats, top). This uniform pattern makes the tool set predictable and easy to navigate, satisfying the consistency requirement even though the suffix is not strictly verb_noun.
Seven tools is well within the ideal range and each one covers a distinct query type for the dataset domain. The count feels neither sparse nor bloated, and every tool has a clear use case.
The set covers metadata, provenance, exact/pattern matching, statistics, ordering, and comparative lookups, which handles most common dataset questions. A minor gap is the absence of a tool to retrieve the full dataset or list distinct values, but agents can work around these with existing tools.