dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the RoughInDesk 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 carries the burden. It implies a read-only schema inspection but does not explicitly state side-effect-free behavior or any other behavioral details beyond the returned items.
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?
A single, direct sentence lists the exact outputs and usage. No fluff or redundancy, and the structure is well-suited for a schema-introspection tool.
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?
The description covers the essential output elements (columns, numeric indicators, row count, provenance banner) and provides clear usage context ('first'), making it sufficiently complete for a simple tool with no output schema.
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?
There are zero parameters, so schema coverage is trivially 100%. The description appropriately adds no parameter details, matching the baseline for no-parameter tools.
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 returns the column list, numeric flags, row count, and provenance banner, explicitly distinguishing it from sibling tools like dataset_stats or dataset_provenance. It also positions itself as the schema-learning entry point.
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?
Explicitly instructs to 'Call this first to learn the schema,' providing clear when-to-use guidance and implying it precedes other operations on the dataset.
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.
Tools are mostly distinct, with clear descriptions for schema, provenance, stats, and top-N queries. Some overlap exists among row retrieval tools (dataset_row, dataset_compare, dataset_search), but descriptions clarify exact match, multi-value match, and substring search.
All tools share the consistent 'dataset_' prefix, but the second part mixes nouns (columns, provenance, row, stats), verbs (compare, search), and an adjective (top), so the naming pattern is not uniform.
Seven tools is well-scoped for a read-only dataset exploration server, covering schema, metadata, lookup, search, comparison, statistics, and top/bottom queries without unnecessary bloat.
The tool set covers the full range of expected dataset operations: schema inspection, provenance, exact row retrieval, substring search, multi-value comparison, numeric stats, and ranking. No obvious gaps for read-only dataset analysis.