dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Netsheetly 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 bears full responsibility for disclosing side effects or safety. It only lists returned data without mentioning whether the operation is read-only, or if it has any side effects or dependencies, leaving room for uncertainty.
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, concise sentence that efficiently enumerates the returned elements and includes a clear call-to-action. No extraneous information is present, and the structure is straightforward.
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?
Given the absence of an output schema and parameters, the description provides a reasonable summary of what the tool returns and advises when to use it. It could be slightly more detailed about the format of the output, but it covers the essential context for initial usage.
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 coverage is 100% (empty). Since there are no parameters to describe, the baseline score of 4 is appropriate, and the description does not need to elaborate on parameter details.
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: columns, numeric flags, row count, and provenance banner. It also explicitly positions itself as the first call to learn the schema, distinguishing its purpose from the 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 phrase 'Call this first to learn the schema' provides a direct usage guideline, indicating that this tool should be invoked before others. However, it does not explicitly contrast with sibling tools, though the recommendation to call first implies a priority.
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 operation: schema, provenance, exact row lookup, substring search, ordered comparison, statistics, and top/bottom extremes. dataset_row and dataset_compare are somewhat similar, but their descriptions make the single-value vs ordered-multi-value distinction clear.
All tools share the consistent dataset_ prefix and snake_case style, making the family recognizable. However, the second segment mixes noun, verb, and adjective forms (columns, compare, top), so the naming is not a strict verb_noun pattern.
Seven tools is well-scoped for a dataset querying server. Each tool covers a different common question type without redundancy, and the count feels neither thin nor bloated.
The tool set covers schema discovery, provenance, exact lookup, substring search, comparisons, summary statistics, and top/bottom ordering. For a read-only dataset QA server, there are no obvious missing operations or dead ends.