dataset_statscount, min, max, mean, median and sum of a numeric column of the Csatzo 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?
No annotations are provided, so the description carries the disclosure burden. It usefully reveals that grouping commas and currency are handled and that non-numeric rows are excluded and counted. It does not specify exact output structure or edge-case behavior, but it goes beyond a simple restatement.
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 with no filler. It front-loads the statistic list and then adds edge-case handling, making it easy to scan and understand quickly.
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 simple single-parameter read tool with no output schema, the description is mostly complete: it names all computed values and key data-handling behavior. It could be more explicit about the response format and behavior for an all-non-numeric or empty column, but these are minor gaps.
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 only defines column with minLength 1, so schema coverage is minimal. The description adds important meaning: the column is numeric and non-numeric values are handled in a specific way. It does not explain column name syntax, but the single-parameter context makes this adequate.
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?
Title and description explicitly state that the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Csatzo dataset. This clearly distinguishes it from siblings like dataset_top or dataset_row.
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?
Usage context is implied: use when summary statistics for a numeric column are needed. However, the description does not explicitly mention alternatives or when not to use it, leaving some differentiation to inference.
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 distinct: schema, provenance, stats, and top each have a unique purpose. The row-returning tools (dataset_row, dataset_search, dataset_compare) could potentially be confused, but their exact-match, contains-search, and multi-value-ordering semantics are described clearly enough to prevent serious misselection.
All seven tools follow the predictable dataset_<operation> pattern, making the set easy to scan and understand. There is no mixing of naming conventions or styles.
Seven tools is a well-scoped size for a dataset exploration server. Each tool covers a distinct useful operation without redundancy or bloat.
The tool set covers the full read-only lifecycle of working with the Csatzo dataset: schema discovery, provenance, exact lookup, free-text search, comparisons, summary statistics, and top/bottom rows. No obvious missing operation would prevent an agent from answering typical questions about the data.