dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Rotazo 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, the description carries the behavioral disclosure burden. It discloses the informational output (columns, numeric flags, row count, provenance banner) and implies a read-only inspection use case, but it does not explicitly state side effects, permissions, or that it is non-mutating. The output-focused description is adequate but not rich.
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 concise, front-loads the core output, and ends with a practical usage directive. All content earns its place, with no redundant phrasing.
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 the simple zero-parameter design and the presence of sibling tools, the description covers the key return contents and the recommended invocation order. It does not specify the exact return shape, but for a schema-discovery tool this is a minor gap.
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. There are no parameter semantics to clarify, and the description adds no unnecessary parameter-related noise.
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 the dataset's columns, numeric column indicators, row count, and provenance banner, and frames it as the schema-learning entry point. It is not a tautology and offers more specificity than the title, though it doesn't explicitly contrast with all 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?
The instruction 'Call this first to learn the schema' provides clear contextual guidance on when to use the tool. It does not explicitly name alternatives or exclusion criteria, but the 'first step' framing is sufficient for a schema-discovery tool.
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 query mode: schema, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom ranking. dataset_row and dataset_compare overlap slightly for single-value exact matches, but the descriptions clearly orient one to single lookups and the other to X-vs-Y comparisons.
All tools share the consistent dataset_ prefix and snake_case style, which makes the family obvious. However, the suffix mixes nouns like columns, provenance, row, stats, and top with verbs like compare and search, so it is not a strict verb_noun pattern.
Seven tools is well-scoped for a dataset-querying server: enough to cover schema, provenance, lookup, search, comparison, statistics, and ranking without feeling redundant. Each tool earns its place.
The tool surface covers the full range of likely questions about the Rotazo dataset, including schema discovery, provenance attribution, exact lookup, substring search, value comparison, numeric summaries, and top/bottom ranking. There are no obvious dead ends for typical exploration or analysis.