dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Stagenix 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 full behavioral disclosure burden. It explains the returned content and the call-first ordering, but does not describe the output format, potential errors, latency, or any side effects. For a zero-parameter metadata tool this is minimally acceptable but not rich.
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 first sentence front-loads the returned information, and the second gives a concise call-order instruction.
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 schema-discovery tool, the description tells the agent what it returns and why to call it first. The only gap is that the exact output structure is not specified, but the absence of an output schema makes this a minor omission.
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 an empty input schema, so there are no parameter semantics to clarify. The description correctly establishes that the tool is about the fixed Stagenix dataset 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?
The description clearly names the Stagenix dataset as the resource and lists the exact outputs: columns, numeric flags, row count, and provenance banner. It does not explicitly differentiate this tool from siblings like dataset_stats or dataset_provenance, so it stops short of a 5.
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 phrase 'Call this first to learn the schema' provides clear, actionable guidance about when to invoke the tool. However, it does not mention alternatives or situations where the tool should not be used.
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 have clear, distinct purposes—schema, provenance, exact-match lookup, substring search, comparisons, statistics, and ranking. dataset_compare and dataset_row overlap somewhat since both filter by column values, but compare is explicitly for ordered multi-value 'X vs Y' queries while row is for a single exact match.
All tools share a consistent dataset_ prefix and use clear, descriptive lowercase names. Even though some are nouns (columns, row, provenance) and others are verbs (compare, search, stats, top), the pattern is uniform and predictable.
Seven tools is well-scoped for a dataset querying server. Each tool covers a distinct mode of interaction—schema discovery, provenance, exact lookup, search, comparison, statistics, and ranking—without unnecessary redundancy.
The tool surface covers the core read-only dataset exploration lifecycle well: understand schema, get provenance, retrieve rows, search, compare, compute statistics, and rank. A minor gap is the absence of a tool for fetching distinct values or arbitrary aggregations, but this is not a critical dead end for the stated purpose.