dataset_statscount, min, max, mean, median and sum of a numeric column of the Orgbix 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 does disclose useful edge-case behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. However, it does not mention how missing values are treated or what happens when the column does not contain numeric data.
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 concise sentence with a parenthetical edge-case note. It is well-structured and contains no unnecessary words or repetition.
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 returned statistics and key edge cases, which is sufficient for an agent to understand the tool's behavior. There is no output schema, but the absence of a formal return format is not critical given the simple output.
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 specifies that 'column' is a required string, so the description adds meaning by clarifying it should be a numeric column. It explains that formatting is handled, but it does not enumerate valid column names or provide examples, leaving some ambiguity.
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 for a numeric column and lists exactly which statistics are returned. It does not explicitly contrast with sibling tools like dataset_top or dataset_search, but the purpose is unambiguous.
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 gives no explicit guidance about when to use this tool versus alternatives such as dataset_search or dataset_top. It mentions handling of formatted numbers and non-numeric rows, but not when the tool should be preferred.
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 have clearly distinct purposes: schema, provenance, stats, top, and three row-query modes. The row-query tools (dataset_row, dataset_search, dataset_compare) are the main source of ambiguity, though their descriptions do clarify the different match semantics.
All tools share the dataset_ prefix and use consistent snake_case, giving a clear family identity. The second part is not uniformly verb-based (columns, provenance, top vs. compare, search), but the pattern is still predictable and readable.
Seven tools is well-scoped for a read-only dataset-access server. Each tool covers a distinct query mode or metadata need without unnecessary redundancy.
The tool surface covers schema discovery, provenance, exact and fuzzy row lookup, comparisons, aggregations, and ranking, so most data-exploration questions are supported. Minor gaps include no general pagination through all rows and no distinct-values tool, but these are workaroundable.