dataset_statscount, min, max, mean, median and sum of a numeric column of the Reviewvo 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 behavioral burden and does well: it discloses that grouping commas and currency symbols are handled during parsing, and that non-numeric rows are excluded and counted. This reveals edge-case behavior an agent would otherwise only discover by calling the tool. It stops short of covering error behavior for unknown columns or exact return shape, so a 5 is not warranted.
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?
A single efficiently packed sentence that front-loads the computed statistics and then adds the two preprocessing caveats. Every element earns its place; no filler.
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 one-parameter tool with no output schema and no annotations, the description covers what is computed and how dirty data is handled. The main gap is the absent return format, but the enumerated statistics strongly imply the output structure, making the description adequate if not exhaustive.
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?
Schema description coverage is 0%, so the description must compensate. It does: 'numeric column of the Reviewvo dataset' clarifies that the sole `column` parameter refers to a column name and should be numeric for meaningful results. It could add expectations about what happens if a non-numeric column is passed, but for a single simple parameter this is solid coverage.
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 names a specific verb-like function ('count, min, max, mean, median and sum') applied to a specific resource ('a numeric column of the Reviewvo dataset'). This clearly differentiates it from siblings like dataset_columns (listing columns) and dataset_search (searching rows).
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 clearly implies the tool is for aggregate summaries of a numeric column, which indirectly separates it from the sibling tools. However, it does not explicitly name alternatives or state when-not-to-use conditions, leaving routing mostly to inference.
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 targets a distinct operation: schema, provenance, exact lookup, substring search, multi-value comparison, aggregate stats, and ranking. Minor overlap exists between dataset_row and dataset_compare (both match column values) and between dataset_columns and dataset_provenance (both expose provenance details), but the descriptions make the intended use clear.
All tools share the dataset_ prefix and use lowercase snake_case, giving a clear family identity. The suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), which is a minor inconsistency, but the overall pattern is predictable.
Seven tools is well-scoped for a single-dataset querying server. Each tool has a distinct role and none feels redundant or unnecessary for the core task of exploring and analyzing the Reviewvo dataset.
The surface covers schema discovery, provenance, exact/string lookup, multi-value comparison, numeric summaries, and ranking, which covers the main ways an agent would explore a dataset. Minor gaps include no pagination for large result sets and no multi-column filter, but these are workable given the available tools.