dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Xlifflane 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 provided, the description carries the full burden of explaining behavior. It discloses the output scope (schema shape, numeric flags, row count, provenance banner) and implies a safe read-only operation. It does not explicitly state that it performs no mutations, but the nature of the tool makes that clear.
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. The first sentence enumerates the returned information, and the second gives a directive. Every word earns 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-inspection tool, the description is nearly complete. It states what is returned and when to call it. It does not describe the exact response format or how this differs from sibling tools, but the absence of parameters and output schema lowers the burden enough that these are minor gaps.
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 coverage is 100%, so the parameter dimension is trivial. The baseline of 4 applies because there are no parameter semantics to explain.
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 what the tool returns: the columns, which are numeric, row count, and provenance banner for the Xlifflane dataset. It goes beyond the title 'Dataset columns and shape' with concrete output components, though it does not explicitly distinguish itself from 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 directive 'Call this first to learn the schema' provides a clear usage sequence, implying it should be used before other dataset tools. It does not mention alternatives or when not to use it, but for a schema-introspection tool with no parameters this is reasonably 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 distinct purpose: schema, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/lowest rows. The main potential confusion is between dataset_row and dataset_compare, since both handle exact value matching, but the multi-value ordering intent of dataset_compare keeps them separable.
All tools share a consistent dataset_ prefix followed by a clear operation or noun: columns, compare, provenance, row, search, stats, top. The naming pattern is uniform and predictable.
Seven tools is a well-scoped set for querying and exploring a single dataset. Each tool covers a distinct need without redundancy, and the count feels neither sparse nor bloated.
The set covers schema inspection, provenance, exact lookups, substring search, multi-value comparisons, numeric statistics, and top/bottom ranking. A minor gap is the lack of a tool to retrieve all rows or page through large result sets, but the existing tools are sufficient for most dataset questions.