dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Requly 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 the returned content (columns, numeric flags, row count, provenance banner) but does not mention potential limitations or side effects, though for a read-only schema tool this is largely sufficient.
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 and well-structured, listing the returned fields and a usage hint without extraneous detail.
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 tool's simplicity, the description fully covers what it does, what it returns, and when to use it. No critical information is missing for an agent to correctly invoke it.
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 describe. Schema coverage is effectively 100%, and the description adds no unnecessary parameter context.
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?
Clearly states it returns columns, numeric flags, row count, and provenance banner, and explicitly positions it as the first call to learn the schema, distinguishing it from sibling tools like stats or top.
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?
Explicitly instructs to call this tool first to learn the schema, providing clear when-to-use guidance relative to other dataset tools.
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 has a distinct query mode: schema discovery, provenance, exact lookup, substring search, ordered multi-value comparison, numeric statistics, and ranking. dataset_row and dataset_search overlap slightly for exact-match cases, but their descriptions clarify the intended use.
All tools consistently use the dataset_ prefix with lowercase snake_case and clear operation names. Minor grammatical inconsistency like dataset_row for plural rows and dataset_top instead of top_rows is present, but the pattern is still predictable.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct question type without redundancy or bloat.
The surface covers schema discovery, provenance, exact/pattern matching, numeric stats, and ranking, which handles most common dataset questions. Missing operations like distinct-value listing or grouped aggregation are minor gaps, not blocking ones.