dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Enrolvo 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 burden. It implies an introspection/read-only operation by saying 'learn the schema' and listing metadata outputs, but it never explicitly states that no data is modified or whether the call has side effects. No contradiction with annotations exists.
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 redundancy; the key outputs are front-loaded and the usage pointer is placed in the second sentence. Every clause contributes.
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 parameterless tool with no output schema, the description covers what is returned and when to call it. It is slightly short on the representation of the numeric-column flags and what exactly a 'provenance banner' looks like, but this is a minor gap for an entry-point metadata tool.
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 description has no parameter meanings to add. The baseline of 4 applies, and the description appropriately focuses on output semantics instead.
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 identifies the resource (Enrolvo dataset) and enumerates the returned contents: columns, numeric flags, row count, and provenance banner. It also frames the tool as the schema-discovery entry point, distinguishing it from siblings like dataset_search or dataset_stats, though it lacks an explicit tool verb such as 'returns'.
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?
'Call this first to learn the schema' is clear usage guidance, positioning this ahead of the other dataset tools. It does not explicitly name alternatives or say when not to use it, but for a zero-argument schema probe the guidance is adequate.
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.
The tools are mostly distinct: schema, provenance, exact lookup, ordered comparison, substring search, stats, and top/bottom are separate concerns. There is minor overlap between dataset_row and dataset_compare for a single exact value, but the descriptions make the intended use cases reasonably clear.
All tools consistently use the dataset_ prefix and snake_case naming. The suffixes are mostly noun-like, with compare and search as verb-like exceptions, but the overall pattern remains predictable and easy to scan.
Seven tools is well-scoped for a read-only dataset query server. Each tool addresses a distinct class of question, and none feel redundant or unnecessary for the stated purpose.
The set covers schema discovery, provenance, exact lookup, multi-value comparison, substring search, numeric aggregation, and ordering. More advanced operations like multi-column filters or distinct-value enumeration are missing but can often be worked around with the provided tools.