dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Dsarvo 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 behavioral disclosure burden. It discloses what the tool returns, which is useful, but does not explicitly state that it is a read-only, side-effect-free operation. However, the nature of a schema-discovery call makes this risk low.
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 filler. The output contents are front-loaded, and the usage directive is concise and actionable.
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 zero-parameter, no-output-schema introspection tool, the description is complete. It explains what the tool returns and when to invoke it relative to other dataset operations, which is all an agent needs to call it correctly.
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. The description adds meaning by describing the output contents even though parameter semantics are not applicable.
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 identifies the resource (the Dsarvo dataset) and the specific outputs: columns, numeric flags, row count, and provenance banner. It also frames the tool as a schema-learning step, which helps distinguish it from siblings like dataset_stats or dataset_provenance, though it does not explicitly name them.
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 clear contextual guidance on when to use the tool. It does not explicitly state when not to use it or name alternatives, but for a zero-parameter discovery tool this is sufficient context.
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.
The metadata and numeric tools (columns, provenance, stats, top) are clearly distinct, but dataset_row, dataset_search, and dataset_compare all retrieve rows by value, so an agent could initially confuse exact-match, contains-search, and multi-value ordering. The descriptions do clarify the boundaries, making the overlap manageable.
All tools share the consistent dataset_ prefix and lowercase style, but the second part mixes nouns (row, columns, provenance, stats) with verbs (compare, search, top). This is predictable enough, though not a strict verb_noun pattern.
Seven tools is well-scoped for a single-dataset server: schema discovery, provenance, three retrieval modes, statistics, and ordering each earn their place without bloat or thinness.
The tool surface covers the main workflows: learn the schema, attribute correctly, look up exact/contains/multi-value rows, compute numeric summaries, and find top/low values. No obvious dead-end operations are missing for the apparent purpose.