dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Clientvo 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 disclosure burden. It clearly states what the call returns, including provenance banner and row count, and for a zero-parameter introspection tool there are no hidden mutation or cost concerns needing disclosure.
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 focused sentence delivers the key output details and usage instruction without repetition or filler. The content is front-loaded and every part contributes value.
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 metadata tool with no output schema, the description gives enough information to invoke it confidently and interpret the result. It could be slightly richer by describing the output shape or naming related siblings, but it is functionally complete.
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 parameter semantics are not applicable in a meaningful way. The description accurately describes what the no-argument call will return, meeting the baseline expected for a parameterless tool.
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 names concrete outputs: columns, numeric indicators, row count, and provenance banner. It also states a clear role—'Call this first to learn the schema'—so an agent can understand exactly what this tool provides and how it fits into a workflow.
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' explicitly positions this tool as the initial schema-discovery step. It does not enumerate sibling alternatives or explicit when-not-to-use cases, but the ordering guidance is strong and actionable.
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 maps to a clear query type (schema, metadata, exact lookup, substring search, multi-value compare, stats, top-N) with only minor overlap between dataset_row and dataset_compare for single-value filtering. The examples in the descriptions help an agent choose correctly.
All seven tools share the dataset_ prefix and use consistent snake_case with a noun/operation suffix (columns, compare, provenance, row, search, stats, top). This creates a predictable pattern that is easy to extend and select from.
Seven tools is appropriate for a dataset query server: enough to cover common exploration tasks without being overwhelming. Each tool has a distinct role, so the count feels well-scoped rather than padded.
The set covers schema discovery, provenance, exact and fuzzy lookup, comparisons, numeric aggregates, and top/bottom queries — the core operations for a single dataset Q&A server. It lacks a direct 'count rows matching a filter' or distinct-values tool, but agents can work around those with existing tools.