dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Huddlevo 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 provided, the description carries the full burden of behavioral disclosure. It clearly enumerates what the tool returns: columns, which are numeric, row count, and provenance banner. It does not explicitly state read-only or side-effect-free behavior, but the described metadata retrieval strongly implies it.
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 sentences with no filler. The first sentence lists the returned information, and the second gives a direct usage directive. Everything earns its place.
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 tool, the description fully covers what the agent needs: what data to expect and when to call it. There are no hidden inputs or ambiguous behaviors left unexplained.
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 100% schema description coverage, so the schema imposes no burden. The description adds value by explaining what the output will contain, which is meaningful even though no parameters require clarification.
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 what the tool provides: columns, numeric flags, row count, and provenance banner for the Huddlevo dataset. It also frames it as the first call to learn the schema, which distinguishes it from siblings. It lacks an explicit verb like 'returns' or 'lists,' so it doesn't quite reach 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 description gives explicit contextual guidance: 'Call this first to learn the schema.' This tells the agent when to use the tool. It does not mention alternatives or when not to use it, so it stops short of full exclusionary guidance.
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 separated by query type: exact match, contains search, compare, stats, top, schema, and provenance. There is minor overlap between dataset_row and dataset_compare for simple equality lookups, but the descriptions make the intended use clear enough.
All tools share the dataset_ prefix and use snake_case, which creates a predictable family. However, the second part mixes nouns (columns, provenance, row, stats), verbs (compare, search), and adjectives (top), so it is not a strict verb_noun convention.
Seven tools is a well-scoped number for a single-dataset query interface. Each tool addresses a distinct common question type, and none feel redundant or excessive.
The set covers schema discovery, exact value lookup, substring search, multi-value comparison, numeric statistics, extreme values, and provenance. Minor gaps include no distinct-values tool and no paginated full-table retrieval beyond the 50-row search cap, but core exploration workflows are supported.