dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Eobify 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 full burden of behavior disclosure. It clearly enumerates the returned information (columns, numeric status, row count, provenance banner), implying a read-only operation, but it does not mention permissions, errors, or output formatting. The core behavior is disclosed, though not richly.
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 two sentences with no filler. It front-loads the output contents and then gives a clear usage directive, making every word earn 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 zero-parameter schema-discovery tool, the description covers the essential outputs and the intended first-step usage. It does not fully specify the output structure, but that is acceptable given the tool's simplicity and the absence of an output schema.
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 no parameter documentation burden. The baseline of 4 applies because the description does not need to add meaning beyond an empty schema.
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 identifies what the tool provides: columns, numeric flags, row count, and provenance banner for the Eobify dataset. It does not use an explicit verb like 'Returns' or 'Lists', but the noun-phrase style is unambiguous and distinct from sibling tools such as dataset_provenance or dataset_stats.
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' gives explicit sequencing guidance, telling the agent this is the initial discovery step. It does not explicitly name alternatives or when not to use it, but for a zero-parameter schema tool the guidance is sufficient.
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 has a clearly defined purpose, but dataset_row and dataset_compare overlap conceptually since both retrieve rows by column value, just with different cardinality and ordering. The other tools are clearly separated between schema, provenance, search, statistics, and top/bottom ranking.
All tool names consistently use the dataset_ prefix followed by a concise operation name in snake_case. The pattern is uniform and predictable, making it easy to infer what each tool does.
Seven tools is a well-scoped set for a single-dataset server. Each tool covers a distinct common query type, and none feel redundant or unnecessary.
The tool surface covers the main dataset exploration needs: schema, provenance, exact lookup, substring search, multi-value comparison, numeric statistics, and top/bottom rows. Minor gaps exist, such as no direct count of filtered rows or grouped aggregation, but agents can work around these with existing tools.