dataset_statscount, min, max, mean, median and sum of a numeric column of the Rebadgo 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 full burden of behavioral disclosure. It explicitly states that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which is valuable. It does not describe error behavior or output format, but the listed statistics implicitly define the return. This is solid coverage for a read-only stats tool.
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 sentence that packs all essential information: the computed statistics, the target dataset, and edge-case handling. There is no redundancy or filler, and the most important information is front-loaded.
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 tool with one parameter and no output schema, the description covers the key aspects: what it computes, the handling of formatting and non-numeric values. It omits potential error cases (e.g., column not found) and does not specify return structure, but these are minor for this scope. Overall, it is sufficiently complete for an agent to use correctly.
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 input schema only declares a required string 'column' with no description, so schema coverage is 0%. The description adds that the column must be numeric and implies it is a column identifier, but it does not specify the exact format (e.g., name vs. index) or provide validation hints. It partially compensates for the schema gap but could be more explicit.
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 enumerates the exact statistical operations (count, min, max, mean, median, sum) and the target resource (numeric column of the Rebadgo dataset). This unambiguously distinguishes it from sibling tools like dataset_top or dataset_row, 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 provides clear context: it's for numeric columns and specifies how data is handled (commas, currency, non-numeric rows). It does not explicitly mention alternatives or when not to use it, but the context is sufficient for an agent to infer its scope. Missing an explicit when-not clause prevents a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool serves a clearly distinct purpose: schema exploration, provenance, exact row lookup, substring search, aggregate stats, top/bottom rows, and value comparisons. Even the similar-looking row and search tools differ in exact match vs. substring match, so agents can reliably choose the right one.
All tools share the consistent prefix 'dataset_' followed by a single, descriptive word (columns, compare, provenance, row, search, stats, top). This uniform pattern makes the tool set predictable and easy to navigate, satisfying the consistency requirement even though the suffix is not strictly verb_noun.
Seven tools is well within the ideal range and each one covers a distinct query type for the dataset domain. The count feels neither sparse nor bloated, and every tool has a clear use case.
The set covers metadata, provenance, exact/pattern matching, statistics, ordering, and comparative lookups, which handles most common dataset questions. A minor gap is the absence of a tool to retrieve the full dataset or list distinct values, but agents can work around these with existing tools.