dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Intakez 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 burden. It discloses the main return contents (columns, numeric flags, row count, provenance banner), which is useful. However, it does not explicitly state that this is a read-only operation, nor does it mention response format, potential errors, or whether it accesses external data.
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, information-dense sentence lists exactly what the tool returns and adds a clear call-to-action. There is no filler or repetition of the title or schema.
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 simple zero-parameter metadata tool with no output schema, the description is complete: it names the dataset, lists the returned information, and positions itself as the first call. An agent has enough context to select and invoke it correctly.
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 and schema description coverage is 100%, so there are no parameter semantics for the description to add. Per the rubric baseline, a score of 4 is appropriate because no parameter documentation is needed.
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 the resource (Intakez dataset) and the specific outputs: columns, numeric flags, row count, and provenance banner. The title adds 'columns and shape'. It does not explicitly use a verb like 'returns' or 'lists', but 'Call this first to learn the schema' makes the purpose unambiguous.
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 description gives explicit usage context: 'Call this first to learn the schema.' This tells an agent when to invoke it relative to other dataset tools. It does not name alternatives or state when not to use it, but for a zero-parameter schema-introspection tool this is adequate guidance.
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 aspect of the Intakez dataset: schema, provenance, exact lookup, substring search, aggregation, ordering, and multi-value comparison. The boundaries are clear and the descriptions specify when to use each.
All seven tools share the dataset_ prefix and use concise lowercase suffixes, creating a predictable pattern. Although some suffixes are nouns and some verbs, the convention is uniform and easy to infer.
Seven tools is a well-scoped set for dataset exploration: schema, metadata, row retrieval, search, stats, top values, and comparisons are all covered without redundancy.
The surface covers the full read-only exploration lifecycle for a dataset: understand schema and provenance, filter rows, aggregate numeric columns, and rank values. No obvious dead ends for typical dataset questions.