dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Topcoatly 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 behavioral disclosure burden. It clearly states the tool is read-only in effect by describing an informational schema-returning operation and identifies the exact data it exposes. It does not discuss limitations or side effects, but none are expected for a metadata tool.
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?
Two concise sentences, front-loaded with the key return data and immediately followed by the recommended call order. Every clause adds information; there is no redundancy or padding.
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 metadata tool, the description fully equips an agent to understand what will be returned and why it should be called first. No critical context is missing.
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 the schema is an empty object covering 100% of the parameters. The description adds relevant context about the dataset and return contents, though there are no parameter semantics to clarify.
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 specifies exactly what the tool returns: columns, numeric flags, row count, and the provenance banner of the Topcoatly dataset. It also positions the tool as the schema-learning entry point, distinguishing 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?
It explicitly instructs 'Call this first to learn the schema', giving a clear usage context. It does not enumerate alternatives or when not to use it, but for a zero-parameter schema introspection 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 targets a distinct query pattern—schema, provenance, exact match, contains search, ordered value comparison, aggregation, and top/bottom ranking. The only mild ambiguity is between dataset_row and dataset_search, but their exact-match versus contains-match descriptions make the boundary clear.
All tools share the consistent dataset_ prefix followed by a clear operation name, and all use lowercase snake_case. The pattern makes the purpose of each tool predictable at a glance.
Seven tools is an appropriate scope for a read-only dataset querying server. Each tool addresses a distinct question type without unnecessary sprawl or redundancy.
The toolkit covers schema discovery, provenance, exact and fuzzy lookup, multi-value comparisons, descriptive statistics, and top/bottom ranking—the core workflows for answering dataset questions. It lacks general range filtering or grouped aggregation, but these are minor gaps for the stated purpose.