dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the HardscapeDesk 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 present, and the description does not explicitly state whether the operation is read-only or has side effects. While it is implied to be a harmless schema retrieval, the absence of explicit transparency about effects leaves some ambiguity.
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 a single, focused sentence that conveys all necessary information without redundancy. It is well-structured and front-loads the core purpose.
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?
Even without an output schema, the description lists the exact information returned (columns, numeric flags, row count, provenance banner) and gives a usage hint. This fully equips an agent to understand the tool's functionality and expected results.
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 no parameters, and the schema coverage is 100% by default. The description correctly omits parameter details, and no additional explanation is needed for this empty parameter list.
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 precisely states the tool's function: retrieving columns, identifying numeric ones, row count, and provenance banner for the HardscapeDesk dataset. It is specific and unambiguous, clearly distinguishing it from sibling tools that perform other operations.
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?
Explicitly instructs to call this tool first to learn the schema, providing clear when-to-use guidance. This helps the agent sequence it appropriately before other dataset operations.
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 clearly distinct operation: schema inspection, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom ranking. No two tools appear to serve the same purpose.
All tool names follow the same 'dataset_' prefix pattern followed by a simple noun or operation name (columns, compare, provenance, row, search, stats, top). This is highly consistent and predictable.
Seven tools is a well-scoped set for a single-dataset query server. Each tool covers a distinct need without redundancy or bloat.
The surface covers schema discovery, data retrieval, search, comparison, statistics, extremes, and provenance. Minor gaps exist, such as no explicit way to retrieve all rows or generate distinct-value lists, but core query workflows are well supported.