dataset_statscount, min, max, mean, median and sum of a numeric column of the Tieoutly 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?
The description reveals important behavioral details: it handles grouping commas and currency, and excludes non-numeric rows while counting them. This gives insight into edge-case handling. Since there are no annotations, the description carries the burden and does so reasonably well, though it does not explicitly mention side effects or read-only nature.
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, well-structured sentence that efficiently lists the statistics and key handling behaviors. It avoids unnecessary words and presents all essential information in a compact form.
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?
The description lists the statistics returned (count, min, max, mean, median, sum) and mentions handling of commas/currency and non-numeric rows. While no output schema is provided, this gives a good understanding of the tool's output. It does not specify the exact return format or error handling, but it is sufficient for typical use.
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 only parameter 'column' has no schema description, so the description must compensate. It does by indicating that the column should be numeric, implying the parameter is the column name. However, it does not provide further details like allowed formats or examples, relying on common sense.
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 that the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column. It distinguishes its function from sibling tools like dataset_row or dataset_search by focusing on statistical aggregation. The mention of handling commas and currency further clarifies its purpose.
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 does not explicitly state when to use this tool versus alternatives. However, the name and functionality imply it should be used when column-level statistics are needed. It lacks explicit contrast with sibling tools such as dataset_columns or dataset_top.
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 has a clearly distinct purpose: schema introspection, exact-match lookup, substring search, multi-value comparison, statistical aggregation, ranking, and provenance metadata. No two tools overlap in functionality, making misselection unlikely.
All tools follow a consistent 'dataset_<descriptor>' pattern, where the descriptor is a noun or verb indicating the operation (columns, compare, provenance, row, search, stats, top). This uniformity aids predictability and discoverability.
Seven tools is well-scoped for a dataset-querying server. Each tool covers a distinct query type or metadata aspect, and none are redundant or unnecessary.
The tool surface covers the primary ways to interact with the dataset: retrieving schema, accessing rows via exact match, substring search, multi-value comparison, computing statistics, finding top/bottom values, and citing provenance. This covers the full lifecycle of typical dataset questions without obvious gaps.