dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Turndownly 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 burden of explaining what the tool returns. It lists the output components but does not state whether the call is read-only, cheap, or how the data is presented, leaving some behavior implicit.
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 sentence that front-loads the key outputs and ends with a clear call-to-action. No filler or repetition.
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 0-parameter schema-introspection tool, the description tells the agent what information will be returned and when to call it. It does not describe the exact response shape, but that is not necessary to invoke the tool successfully.
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?
There are zero parameters and the schema coverage is effectively complete, so the baseline is 4. The description adds no parameter details, but none are 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 names a specific, concrete result set: columns, their numeric status, row count, and provenance banner. It also marks the tool as the first schema-learning call, which clearly separates it from siblings like dataset_row and 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?
'Call this first to learn the schema' is an explicit usage directive. It lacks when-not-to-use guidance or alternatives, but for a lightweight 0-parameter introspection tool this is sufficient context.
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 clear primary purpose: schema, provenance, exact lookup, substring search, comparison, stats, and top/bottom ranking. There is minor overlap between dataset_row and dataset_compare for single-value lookups, but the descriptions steer usage toward distinct cases.
All tools share the dataset_ prefix and use a consistent noun or verb suffix after it. The pattern is predictable and makes it easy to infer what each tool does.
Seven tools is a well-scoped set for querying and exploring a single dataset. Each tool addresses a different query type without unnecessary redundancy.
The surface covers schema discovery, provenance, exact matching, substring search, comparisons, numeric statistics, and ranking. Minor gaps like listing all unique values or arbitrary numeric filtering are absent, but they can be worked around with the existing tools.