dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Upkeepvo 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 present, so the description must carry behavioral context. It states the returned metadata and implies a read-only schema inspection, but it does not explicitly say there are no side effects, how large the response is, or how the provenance banner relates to dataset_provenance.
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 carry the full payload: the first front-loads all output contents, the second gives the action instruction. Every word earns its place and no structured information is repeated.
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 parameterless metadata tool with no output schema, the description covers what the agent gets and when to call it, which is sufficient for correct invocation. Minor gaps remain around the meaning of the provenance banner and the exact return format, but these are not blocking.
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 input schema has zero properties, so there are no parameters to disambiguate; the baseline for a parameterless tool is satisfied. The description needs no parameter-level detail and does not waste space on it.
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 lists the concrete contents returned—columns, numeric flags, row count, and provenance banner—and explicitly frames the tool as the first schema-learning step for the Upkeepvo dataset. It lacks a direct action verb like 'returns', but the resource and scope are unmistakable and distinct from siblings.
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' provides explicit sequencing guidance that orients the agent before using other dataset tools. It does not spell out when to prefer dataset_provenance or dataset_stats, so it stops short of full alternative routing.
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 purpose: schema discovery, comparison, provenance, exact row lookup, substring search, stats, and ranking. No two tools overlap in functionality, making misselection unlikely.
All tools follow the consistent pattern 'dataset_' followed by a lowercase noun or verb (columns, compare, provenance, row, search, stats, top). The naming is uniform and predictable.
With 7 tools, the set is well-scoped for a dataset querying server. Each tool covers a distinct query need without redundancy or bloat.
The tools provide comprehensive read-only access to the dataset: schema, row retrieval, search, statistics, ranking, comparisons, and provenance. No obvious gaps for typical analysis workflows.