dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Duesvo 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 provided, the description correctly carries the disclosure burden by enumerating the returned information: columns, numeric columns, row count, and provenance banner. It does not describe side effects, error behavior, or whether the operation is read-only, but for a schema-discovery call this is a reasonably clear behavioral picture.
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 two short sentences with no filler. The key output details are front-loaded, and the usage guidance is a separate, direct call to action. Every word earns its place.
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?
This is a simple zero-parameter tool with no output schema, so the description must communicate return value substance on its own. It lists the main return elements, which is sufficient for an agent to know what to expect. It could add a bit more about the format of the provenance banner, but overall the context is complete enough.
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, so the baseline is 4. The description does not need to document parameter meaning; instead, it focuses on what the tool returns, which is appropriate.
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 provides: the columns, numeric flags, row count, and provenance banner of the Duesvo dataset. It is distinct enough from siblings like dataset_provenance and dataset_stats because it returns schema-focused structural information, though it does not explicitly name and contrast 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 instruction 'Call this first to learn the schema' gives clear context for when the tool should be used as an initial discovery step. It does not explicitly mention alternatives or exclusions, but the guidance is direct enough for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools have clearly distinct purposes: schema, provenance, exact match, substring search, multi-value comparison, aggregate stats, and top/bottom ranking. The minor overlap between dataset_row, dataset_search, and dataset_compare could cause occasional misselection, but each description states its exact matching behavior.
All tools share the dataset_ prefix, which creates a strong family resemblance. However, the suffix is sometimes a noun (columns, provenance, row) and sometimes a verb (compare, search, stats, top), so the pattern is not fully uniform.
Seven tools is a well-scoped set for querying a single dataset. Each tool addresses a distinct common question type without redundancy or bloat.
The set covers schema discovery, provenance, exact lookups, fuzzy search, controlled comparison, numeric aggregates, and sorted extremes. A general arbitrary filter or grouped analysis is missing, but the provided tools handle the most likely dataset questions.