dataset_statscount, min, max, mean, median and sum of a numeric column of the WallChartBook 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 behavioral burden. It adds useful context by saying grouping commas and currency are handled and non-numeric rows are excluded and counted. It does not describe the output structure or edge cases such as an all-non-numeric column, so it is adequate but not comprehensive.
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 entire description is one dense, well-organized sentence. The metric list is front-loaded, and the edge-case handling is appended without padding or repetition.
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, the description supplies the returned metrics and the key data-cleaning behavior. It lacks only an explicit pointer to column discovery and the exact return format, but the low complexity makes this adequate for correct invocation.
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 coverage is 0%, so the description must compensate. It clarifies that the single `column` parameter expects a numeric column from the WallChartBook dataset, which is valuable. It does not explain how valid column names are formatted or where to discover numeric columns, leaving a partial gap.
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 enumerates exactly what the tool computes (count, min, max, mean, median, sum) for a numeric column, making the purpose unmistakable. It clearly distinguishes this tool from row/search/top/columns siblings by focusing on summary statistics.
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 intended use is strongly implied by the title and description: use this when you need summary statistics for a numeric column. However, it never explicitly names alternatives or states when not to use it, leaving the routing decision 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.
Most tools have clearly distinct purposes, but dataset_row and dataset_compare overlap for single-value equality checks, and dataset_search's substring match can also return exact matches. The descriptions largely resolve this, so the ambiguity is minor.
All tool names use the same dataset_ prefix and lowercase snake_case, which is predictable and clean. However, the suffix mixes nouns (columns, row, stats, provenance) with verbs (compare, search) and the adjective-like 'top', so it is not a fully consistent verb_noun pattern.
Seven tools is an appropriate size for a dataset-query server; each tool covers a distinct query pattern (schema, exact lookup, search, comparison, stats, ranking, provenance) without redundancy or bloat.
The server covers the core read-only workflows for the dataset: schema discovery, row retrieval, text search, comparisons, numeric summaries, top/bottom rankings, and citation metadata. It lacks a way to list all rows or get distinct column values, but these are workable gaps rather than dead ends.