dataset_statscount, min, max, mean, median and sum of a numeric column of the Focusvo 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 usefully reveals that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted, which addresses common edge cases. It does not mention behavior for empty columns or nonexistent columns, but the disclosed details add meaningful transparency.
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 that leads with the output statistics and packs useful edge-case handling into a parenthetical. Every word contributes value with no redundancy.
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 covers the core operation, output statistics, and important data-cleaning behavior. It is slightly incomplete regarding error handling for empty or fully non-numeric columns, but overall it gives an agent enough to use the tool correctly in typical cases.
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 defines column as a non-empty string, and schema description coverage is 0%. The description adds context that the column must be numeric and belong to the Focusvo dataset, but it does not explain valid column name formats or how to discover available columns (e.g., via dataset_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 identifies the tool's function: computing six named summary statistics (count, min, max, mean, median, sum) for a numeric column in the Focusvo dataset. This distinguishes it from sibling tools like dataset_search, dataset_row, or dataset_top, 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 is for obtaining numeric summary statistics, but it does not explicitly state when to use it versus alternatives such as dataset_top or dataset_columns. No exclusions or alternative-routing guidance is provided.
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 specific purpose: schema, provenance, exact lookup, multi-value comparison, search, stats, and top-N. dataset_compare and dataset_row overlap somewhat since both filter rows by column values, but the multi-value ordered behavior of dataset_compare and the exact single-value behavior of dataset_row keep them mostly distinct.
All tool names follow the same dataset_<noun> pattern, making the set predictable and easy to navigate. The naming clearly signals that every tool operates on the same Focusvo dataset.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct mode of querying or metadata retrieval without redundancy or unnecessary bloat.
The set covers schema discovery, provenance, exact lookup, search, comparison, statistics, and top-N queries, which are the core operations for exploring a dataset. Minor gaps exist such as distinct-value enumeration or grouped aggregation, but agents can work around them with existing tools.