dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Depreo 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. It discloses what the tool returns (columns, numeric flags, row count, provenance banner) but does not explicitly state whether it is read-only, has side effects, or requires authentication. However, the phrase 'learn the schema' implies a safe, informational query, which provides some transparency.
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 sentences, front-loading the purpose and ending with a clear usage directive. Every word contributes value with no fluff or repetition.
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 simple, parameterless tool with no output schema, the description fully covers what the agent needs: it lists the output components and explicitly states when to call it (first). This is sufficient for an agent to invoke it correctly without ambiguity.
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 there is nothing to document. The description adds no parameter semantics because none exist, and the baseline for 0 params is 4. The description's mention of specific output fields aligns with the schema's absence of inputs.
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 resource (Depreo dataset) and the specific information it provides: columns, numeric flags, row count, and provenance banner. It also says 'Call this first to learn the schema,' which distinguishes it from sibling tools and signals its role as the introductory schema tool.
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 directive 'Call this first' provides explicit timing guidance, making it clear this is the initial step before using other dataset tools. It doesn't explicitly say when not to use it, but the strong 'first' instruction effectively communicates its place among alternatives.
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.
Most tools are clearly distinct: schema, provenance, exact row lookup, search, stats, top-N, and comparison each serve different purposes. The only mild ambiguity is between dataset_compare and dataset_row, since both retrieve rows by column values, though descriptions clarify ordered multi-value vs exact single-value use.
All tools share a consistent dataset_ prefix and snake_case convention, making them easy to recognize as part of one set. However, the suffix mixes nouns like columns, row, and stats with verbs like compare and search, so it does not follow a uniform verb_noun pattern.
Seven tools is well-scoped for a dataset querying server. Each tool covers a distinct common operation—schema, provenance, lookup, search, stats, top-N, and comparison—without unnecessary bloat or redundancy.
The tool surface covers the core dataset exploration workflow well: schema, provenance, exact and fuzzy lookup, numeric statistics, top-N ranking, and row comparisons. Minor gaps like arbitrary aggregation or pagination exist, but they are not critical for typical dataset questions.