dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Taxyearvo 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. It discloses the exact output contents (columns, numeric flags, row count, provenance banner) and that it is a schema-inspection tool. It does not mention side effects, but as a read-only metadata tool, this is sufficient transparency.
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, front-loaded with the key output details and the primary usage instruction. No wasted words; every clause 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 metadata inspection tool with no output schema, the description fully covers what the agent needs to know: what it returns and when to call it. It is complete and self-sufficient.
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 the schema covers 100% (empty properties). The description adds value by explaining what the tool returns, which is more than the schema alone. With 0 params, the baseline is 4, and the description does not need 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 states the tool returns columns, numeric flags, row count, and provenance banner for a specific dataset (Taxyearvo), and explicitly says to call it first to learn the schema. This distinguishes it from siblings like dataset_stats or dataset_top.
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 provides clear usage context: 'Call this first to learn the schema.' It implies this is the initial step before using other tools, but does not explicitly name alternatives or exclusion criteria. Still, the guidance is direct and actionable.
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.
Each tool targets a distinct data-access operation: schema intro, provenance, exact lookup, substring search, value comparison, statistics, and extreme rows. dataset_compare and dataset_row are somewhat similar in that both filter on column values, but their descriptions clarify the multi-value ordered behavior versus exact equality.
All tools share a consistent dataset_ prefix and lowercase snake_case naming style, making the set predictable. Although the names are nouns rather than verbs, the convention is uniform and easy to infer.
Seven tools is a well-scoped size for a dataset-focused MCP server. Each tool addresses a distinct query or metadata need without excessive overlap or unnecessary bloat.
The surface covers the main dataset operations: schema, provenance, row lookup, text search, multi-value comparison, numeric stats, and top/bottom rows. Minor gaps like range filtering, grouping, or paginated full scans exist but most typical questions about this dataset can be answered.