dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Hydrantly 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 carries the burden of explaining behavior. It discloses the main outputs: columns, numeric indicators, row count, and provenance banner. It does not discuss side effects or failure modes, but as a zero-parameter read-style metadata tool, this level of transparency is adequate.
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 with no wasted words. It front-loads the core output contents first and then gives clear usage guidance. Every phrase 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?
For a simple introspection tool with no parameters and no output schema, the description covers enough: what the tool returns and when to call it. It leaves minor details like exact output formatting unspecified, but the essentials for correct invocation are present.
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. There are no parameter semantics to clarify, and the description appropriately focuses on the output rather than 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 tool's purpose: learn the schema of the Hydrantly dataset by returning columns, numeric flags, row count, and provenance banner. It is specific about the resource and what is returned, though it does not explicitly differentiate itself from sibling tools like dataset_provenance, which may overlap on the provenance banner.
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 a clear and explicit usage context: this is the entry point for understanding the dataset. It does not mention alternatives or when not to use it, but for a zero-parameter schema-inspection tool this is sufficient.
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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Each tool targets a distinct data access pattern: schema, provenance, exact row lookup, fuzzy search, comparisons, top/bottom rows, and numeric stats. There is mild overlap between dataset_row and dataset_compare since both filter by column values, but their intended use cases are clearly differentiated.
All tools share a consistent dataset_ prefix and use snake_case, which makes the set look cohesive. However, the suffixes mix nouns (columns, row, stats, top, provenance) with verbs (compare, search), so the pattern is not perfectly uniform.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a meaningful access mode without unnecessary redundancy or overwhelming the agent.
The tool set covers the core data exploration lifecycle: schema discovery, metadata, exact lookup, search, comparison, top/bottom ranking, and summary statistics. It lacks arbitrary multi-condition filtering or grouped aggregations, but those are not clearly required for the stated dataset Q&A purpose.