dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Retainvia 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, the description carries the burden of disclosure. It transparently lists what the tool returns (columns, numeric flags, row count, provenance banner), which implies a read-only inspection operation. It does not explicitly state side effects, but none are indicated, and the nature of the tool suggests no destructive behavior.
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 concise, consisting of two short sentences. It packs essential information: what is returned (columns, numeric flags, row count, provenance) and when to call it (first). There is no redundant or extraneous text.
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 there is no output schema, the description adequately explains the output content and purpose. It mentions all key elements an agent would need to know: the dataset name, the type of information (columns, types, row count, provenance), and the recommended invocation order. This provides sufficient context for correct 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% with an empty properties object. Per the rubric, a baseline of 4 is appropriate since there are no parameters to describe. The description correctly implies that no input is 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 the columns, numeric column indicators, row count, and provenance banner of the Retainvia dataset. It also explicitly says to call this first to learn the schema, which effectively distinguishes it from sibling tools that handle comparing, searching, or 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?
The description provides explicit usage guidance: 'Call this first to learn the schema.' This instructs the agent on when to invoke this tool relative to others, implying it is a prerequisite for understanding the dataset structure. No alternative tools are mentioned, but the prioritization is clear.
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 distinct purposes, but dataset_row, dataset_search, and dataset_compare all retrieve rows via value matching, creating possible misselection. The descriptions help clarify exact vs. contains vs. list comparison, but boundaries are not crisp.
All tools share a consistent dataset_ prefix and snake_case formatting, making the group recognizable. However, the suffix mixes nouns (columns, row, stats, provenance) with verbs (compare, search, top), so the pattern is not fully uniform.
Seven tools is a well-scoped set for exploring and querying a single dataset. Each tool covers a distinct need without bloat or redundancy at the set level.
The toolset covers schema inspection, provenance, stats, exact lookup, substring search, comparisons, and top/bottom ordering, which forms a solid read-only exploration surface. Minor gaps exist, such as multi-condition filtering or grouped aggregations, but agents can likely work around them.