dataset_statscount, min, max, mean, median and sum of a numeric column of the Consentvia 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, but the description discloses important operational behavior: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond a simple summary and gives the agent meaningful expectations about edge-case handling.
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 front-loads the list of statistics and includes the important caveats in a parenthetical. There is no wasted text, and every phrase contributes to understanding the tool's behavior.
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 tool with one parameter and no output schema, the description covers the dataset context, acceptable input type, and handling of malformed rows. It does not explicitly describe the output format, but the listed statistics strongly imply what will be returned, making the description sufficiently complete.
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 is 'column', and the schema simply requires a non-empty string. The description adds value by specifying that the column should be numeric and by explaining how non-numeric data is treated, which helps the agent understand what kind of input is valid.
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 tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Consentvia dataset. This is specific enough to distinguish it from the sibling tools like dataset_search, dataset_row, and dataset_top based on name and 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 provides no explicit guidance on when to choose dataset_stats over alternatives such as dataset_top or dataset_search. There are no usage conditions, exclusions, or mentions of alternative tools, leaving the agent to infer applicability solely from the tool's name and description.
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, search, stats, and top each target a different operation. The only ambiguity is between dataset_row and dataset_compare, which both filter rows by column values, though compare explicitly handles multiple values and ordering.
All tools share a consistent dataset_ prefix and use short, descriptive nouns or verbs that align with their function. The naming pattern is uniform and predictable, making it easy to infer what each tool does.
Seven tools is well-scoped for a read-only dataset querying server. Each tool covers a distinct useful query pattern without unnecessary redundancy or bloat.
The server covers the core dataset exploration lifecycle: schema inspection, provenance, exact lookups, multi-value comparisons, text search, numeric statistics, and top/bottom sorting. Minor gaps exist such as pagination or arbitrary multi-condition filtering, but most common questions about the dataset can be answered.