dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Attestroom 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 burden of explaining behavior. It discloses the key output elements, but it does not explicitly state that the tool is read-only, nor does it describe the output format or any error/edge-case behavior. This is adequate for a zero-parameter schema-inspection tool but not richly transparent.
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?
The description is two sentences with no wasted words. The output contents are front-loaded, and the usage guidance is brief and actionable.
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?
Even without an output schema, the description names the essential return items: columns, numeric flags, row count, and provenance banner. It is complete enough for an agent to understand what it will get and why this should be the first call, though the provenance banner concept could use a little more explanation.
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 with 0 parameters, so parameter semantics are not a concern. Per the baseline for zero-parameter tools, the description does not need to compensate for missing parameter documentation.
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 what the tool returns: the columns, their numeric status, row count, and provenance banner for the Attestroom dataset. The closing instruction, 'Call this first to learn the schema,' gives it a distinct role among siblings, though it lacks an explicit verb like 'returns' or 'lists.'
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 guidance on when to invoke the tool: before exploring the dataset. It does not mention alternatives or exclusions, but the precedence instruction is clear enough for an agent to sequence it correctly.
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 have clearly distinct purposes: schema, provenance, exact lookup, substring search, stats, and top/bottom queries. dataset_row and dataset_compare overlap somewhat since both filter on column values, but the descriptions clarify exact single-value matching versus ordered multi-value comparison.
All tools consistently share the dataset_ prefix and use lowercase snake_case, which makes the set feel unified. However, suffixes are a mix of nouns (columns, provenance, row, stats) and verbs (compare, search), so the pattern is not perfectly uniform.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a distinct common operation without feeling padded or redundant.
The set covers the essential dataset operations: schema discovery, provenance, exact match, text search, numeric statistics, ranking, and multi-value comparison. Minor gaps exist such as pagination for search results and range-based numeric filters, but agents can generally answer common questions without dead ends.