dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Mashzo 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 full burden of behavioral disclosure. It states the tool's return content and the fact that it is a schema-learning step, but does not explicitly mention read-only behavior, side effects, or any caveats. For a metadata tool, the lack of side-effect disclosure is a minor gap.
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, dense sentence that packs in all essential information: what it returns, the dataset name, and a usage directive. No wasted words, and the key purpose is front-loaded.
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 provides sufficient context: what information is returned and when to use it. It does not detail the exact format of the output, but that is not critical for such a simple retrieval 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 and the schema is empty (100% coverage). The description does not need to explain parameters, and the baseline for 0-param tools is 4. No additional parameter semantics are required.
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 the tool's purpose: it returns columns, numeric flags, row count, and a provenance banner for the Mashzo dataset. It also instructs to call it first to learn the schema, which distinguishes it as an entry point among siblings.
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 says 'Call this first to learn the schema,' giving a clear when-to-use directive. It does not explicitly mention alternatives or when not to use, but the priority instruction is strong enough to guide the agent.
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.
The tools are largely distinct: schema, provenance, exact lookup, substring search, compare, stats, and top-k. The only mild overlap is among dataset_row, dataset_search, and dataset_compare, since they all retrieve rows, but their descriptions clarify exact match, contains, and value-list comparison respectively.
Every tool uses a consistent `dataset_` prefix with clear snake_case names. Even though some suffixes are nouns and some are verbs, the pattern is uniform and predictable across the entire tool set.
Seven tools is a well-scoped size for a dataset querying server. Each tool covers a distinct common operation without feeling redundant or excessive.
The surface covers the core dataset operations well: schema inspection, provenance, exact lookup, search, comparisons, statistics, and top/bottom ranking. Minor gaps include distinct-value enumeration and grouped aggregation, but these are not fatal for typical dataset questions.