dataset_statscount, min, max, mean, median and sum of a numeric column of the OrderPadLedger 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 provided, the description carries the behavioral disclosure burden. It does well by explaining that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. It does not detail output shape or error behavior, but the disclosed quirks are meaningful and non-obvious.
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 compact sentence that lists the computed statistics first and then adds relevant caveats. Every part carries information and there is no 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?
For a simple one-parameter tool with no output schema, the description is largely complete: it names the dataset, the statistics, and important data-cleaning behavior. It could mention the return format or edge cases, but the core calling context is sufficiently covered.
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 schema only documents a required string column with no description, and schema coverage is 0%. The description adds that the column must be numeric and belong to the OrderPadLedger dataset, which is useful, but it stops short of explaining exact column-name expectations or what happens for invalid columns.
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 operation: computing count, min, max, mean, median, and sum for a numeric column of the OrderPadLedger dataset. This makes the tool's job concrete and distinguishes it from siblings like dataset_row or dataset_search, which serve different purposes.
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 the tool should be used for numeric summary statistics on the OrderPadLedger dataset, but it does not explicitly say when to prefer this over siblings or when not to use it. There is no mention of alternatives or exclusions.
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 distinct role: schema, provenance, exact-match row lookup, substring search, multi-value comparison, numeric stats, and top/bottom ranking. The only mild overlap is dataset_row versus dataset_compare and dataset_search, but their descriptions clarify exact equality, multi-value filtering, and cell containment.
All tools share the dataset_ prefix, making the family immediately recognizable. The suffixes mix nouns (columns, row, provenance, stats, top) and verbs (compare, search), so there is no strict verb_noun convention, but the pattern is predictable and readable.
Seven tools is a well-scoped count for a single-dataset exploration server. Each tool covers a distinct query need without redundancy or unnecessary surface area.
The server covers the full read-only lifecycle of interacting with this dataset: schema discovery, provenance, row lookup, search, comparison, aggregation, and ranking. There are no obvious missing operations for its stated purpose.