dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Defectbird 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 must carry the behavioral disclosure burden. It discloses the contents of the response, including columns, numeric flags, row count, and provenance banner, and implies a read-only metadata operation by saying 'call this first'. However, it does not explicitly address side effects, permissions, or possible failure modes.
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 short sentences with no filler. The first sentence enumerates the returned information, and the second provides a clear usage 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, no-output-schema tool, the description is complete: it states what the agent gets, identifies the dataset, and instructs the agent to call it first. There is nothing critical missing for selecting and invoking this 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 there is no parameter documentation burden. The description adds value by enumerating what the agent will learn from the call, which is more meaningful than an empty input schema alone.
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 (Defectbird dataset) and the returned information: columns, numeric flags, row count, and provenance banner. It lacks an explicit verb like 'returns', but the phrase 'Call this first to learn the schema' makes the tool's role unambiguous and distinguishes it 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 description explicitly tells the agent to call this tool first to learn the schema, establishing a clear usage context. It does not explicitly describe when not to use it or name alternatives, but for an initial schema-discovery tool this 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 distinct type of access: schema, provenance, exact row lookup, substring search, multi-value comparison, statistics, and top/bottom ranking. dataset_row and dataset_compare are somewhat related, but the descriptions make the intended use clear.
All tools use a consistent dataset_ prefix followed by an operation noun or verb such as columns, compare, search, stats, and top. The naming pattern is predictable and makes the tool purpose easy to infer.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct data access need without redundancy or bloat.
The set covers schema discovery, provenance, exact lookup, substring search, comparisons, statistics, and top/bottom ordering, which covers most dataset Q&A workflows. There is no general-purpose filter or pagination tool, but the provided operations form a coherent query surface.