dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Handovra 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, the description carries the burden of explaining behavior. It discloses the exact data returned—columns, numeric indicators, row count, and provenance banner—and implies a read-only schema inspection. It omits explicit side-effect or error details, but for a no-parameter schema tool that is a minor gap.
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 lists the returned content, the second gives a clear usage directive. It is front-loaded and every sentence 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 tool with no output schema, the description covers what the agent will receive and when to call it. It leaves the exact return format and the relationship to dataset_provenance unspecified, but the essential context is present.
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 is empty, so the baseline is 4. The description appropriately adds context about what the dataset schema contains rather than needing to explain parameters.
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 lists what the tool returns—columns, which are numeric, row count, and provenance banner—for the Handovra dataset. It lacks an explicit verb like 'returns', but 'learn the schema' makes the purpose unmistakable.
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' is an explicit usage instruction that establishes this tool as the entry point before other dataset tools. It does not name alternatives or state when not to use it, so it stops short of a 5.
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 differentiated by operation: schema, provenance, exact match, substring search, multi-value comparison, stats, and ranking. The only minor overlaps are dataset_row vs dataset_compare for single-value lookups and the shared metadata banner in dataset_columns vs the full provenance record.
All tools share a consistent lowercase dataset_ prefix and snake_case style, making the family immediately recognizable. The suffixes mix nouns (columns, provenance, row, stats) with verbs (compare, search), so it is not a strict verb_noun pattern, but the deviation is minor and readable.
Seven tools is well within the ideal range for a read-only dataset exploration server. Each tool covers a distinct query need without redundancy, and none feels like filler.
The set covers the core data-exploration lifecycle: schema discovery, provenance, exact lookup, text search, multi-value comparison, numeric summaries, and top/bottom ranking. It lacks direct numeric range filtering or group-by, but agents can often approximate these with top, stats, or search, so the gaps are minor rather than blocking.