dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Lessonvo 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 full burden of behavioral disclosure. It implies a read-only metadata probe by describing schema learning and output contents, but it does not explicitly state read-only behavior, permissions, or side effects. The 'learn the schema' phrasing partially compensates for this 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?
The description is a single sentence that front-loads the specific output items and ends with a clear directive. Every clause contributes value, with no redundancy or filler.
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?
With no parameters and no output schema, the description's enumeration of outputs (columns, numeric indicators, row count, provenance banner) is sufficient for an agent to call the tool and interpret results. It could explicitly state read-only behavior, but the tool's simple metadata role makes the current information largely complete.
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 document. The description adds context about what the returned schema contains, satisfying the baseline expectation for a 0-parameter tool.
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 concretely lists the outputs (columns, numeric flags, row count, provenance banner) for the Lessonvo dataset, making the purpose clear. It lacks an explicit action verb like 'returns' or 'lists,' but 'Call this first to learn the schema' clarifies the operation and differentiates it from sibling tools.
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 an explicit timing directive: 'Call this first to learn the schema.' This tells an agent when to use the tool relative to other dataset operations. It does not name alternatives or exclusions, but the 'first call' guidance is strong and contextually clear.
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 target clearly distinct operations: schema, provenance, exact match, multi-value match, substring search, stats, and top-N. The only potential confusion is between dataset_row and dataset_compare, which both fetch matching rows but differ in single vs. multiple values — the descriptions make this distinction reasonably clear.
All tools share the consistent 'dataset_' prefix in snake_case, which is good. However, the suffix mixes nouns (columns, provenance, row, stats) with verbs (compare, search), and 'dataset_top' is cryptic while 'dataset_row' is singular despite returning rows.
Seven tools is well-scoped for a single-dataset query server. Each tool earns its place covering a distinct query type: schema, attribution, exact lookup, set membership, substring search, aggregation, and ranking.
The surface covers schema, provenance, exact/partial lookup, comparison, stats, and top-N queries well. Obvious gaps include no way to page through or list all rows, no multi-condition (AND) filtering, and no group-by counts — limitations that may force agents to work around when answering comparison questions.