dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Reputzo 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 and implies a read-only schema-inspection call, but it does not explicitly state side effects, permissions, or return formatting. For a parameter-less schema tool this is adequate but not exhaustive.
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?
Two short sentences: the first lists the returned information, the second gives a crisp usage directive. No wasted words, and the key content is front-loaded.
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?
With no parameters and no output schema, the description gives the essential return contents plus a clear usage instruction. It could go further by describing the exact output format, but that is a minor gap for a simple schema-probe tool.
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 no parameter explanations are needed and the baseline of 4 applies. The description correctly avoids inventing parameter guidance where none exists.
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 names the exact outputs: columns, numeric columns, row count, and provenance banner of the Reputzo dataset. It clearly positions this as the schema-discovery entry point, though it does not explicitly contrast it with sibling tools such as dataset_provenance.
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?
'Call this first to learn the schema' gives an explicit, unambiguous invocation context: an agent should use this before other dataset tools. It doesn't spell out when not to use it or name alternatives, but the timing guidance is strong.
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, stats, top), but dataset_row and dataset_compare can be confused since both filter by column values — the exact vs. multiple-values distinction is subtle, though search is clearly different with substring matching.
All tools follow the dataset_ prefix with a clear noun (columns, compare, provenance, row, search, stats, top), making the naming pattern perfectly consistent and predictable.
Seven tools is well-scoped for querying a single dataset, covering schema, content, search, comparison, statistics, ranking, and provenance without unnecessary bloat.
The surface covers all common dataset query operations (schema, lookup, filtering, search, stats, ordering, provenance), but there is no tool for aggregating by groups or listing dataset versions, which are minor gaps.