dataset_statscount, min, max, mean, median and sum of a numeric column of the Taxyearvo dataset (grouping commas and currency are handled; non-numeric rows are excluded and counted).
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes |
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 full burden. It discloses that grouping commas and currency are handled, and non-numeric rows are excluded and counted. This is valuable behavioral context beyond the bare operation. It doesn't cover error conditions or output format, but the disclosed handling is significant.
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, information-dense sentence. The list of statistics is front-loaded, and the parenthetical adds processing details. It's efficient with no fluff, though the parenthetical makes it slightly longer than necessary.
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?
Given one parameter, no output schema, and no annotations, the description covers the main operation, data handling, and dataset reference. It doesn't describe the return format explicitly, but listing the statistics implies the output structure. It's largely complete for an agent to invoke correctly.
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?
Schema coverage is 0%, so the description must compensate. It indicates the parameter is a numeric column of the Taxyearvo dataset, giving the agent context that it's a column name. Though it doesn't explicitly say 'the name of a numeric column', the phrasing 'numeric column of the Taxyearvo dataset' conveys the required semantic.
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 computes summary statistics (count, min, max, mean, median, sum) for a numeric column of a specific dataset. It distinguishes from siblings by specifying the exact output (stats) and the dataset name, making it obvious it's not about column listing, search, or row retrieval.
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 implies when to use it (for numeric column statistics) but does not explicitly mention alternatives or exclusions. While the sibling names give context, the description itself doesn't state when to prefer this over dataset_top or dataset_search. The usage guidance is implied but not explicit.
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.