dataset_statscount, min, max, mean, median and sum of a numeric column of the Requly 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 discloses non-trivial behaviors: it handles grouping commas and currency, and excludes non-numeric rows while counting them. However, it does not state whether the tool is read-only, whether it can fail, or any side effects, and there are no annotations to supplement this information.
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 covers all essential information without redundancy. It lists the computed statistics and notes special handling, making it both concise and well-structured.
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?
Given the low complexity (one required parameter, no output schema), the description provides adequate context for the purpose and expected behavior. However, it omits details about the return format or potential errors, and does not reference the dataset schema, leaving some gaps for an agent.
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 provides only the parameter name 'column' with no description, and the tool description does not explicitly explain what the 'column' parameter represents (though it is inferable). Given the 0% schema coverage, the description should compensate but does not sufficiently clarify the parameter's semantics.
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 explicitly enumerates the statistical operations (count, min, max, mean, median, sum) and identifies the target resource (numeric column of the Requly dataset), making the tool's purpose unambiguous. It inherently distinguishes itself from sibling tools like dataset_top or dataset_search, which have different focuses.
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 guidance on when to use this tool instead of alternatives. It does not mention conditions like 'when you need summary statistics' or compare with sibling tools, leaving the selection decision entirely to the agent.
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 query mode: schema discovery, provenance, exact lookup, substring search, ordered multi-value comparison, numeric statistics, and ranking. dataset_row and dataset_search overlap slightly for exact-match cases, but their descriptions clarify the intended use.
All tools consistently use the dataset_ prefix with lowercase snake_case and clear operation names. Minor grammatical inconsistency like dataset_row for plural rows and dataset_top instead of top_rows is present, but the pattern is still predictable.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct question type without redundancy or bloat.
The surface covers schema discovery, provenance, exact/pattern matching, numeric stats, and ranking, which handles most common dataset questions. Missing operations like distinct-value listing or grouped aggregation are minor gaps, not blocking ones.