dataset_statscount, min, max, mean, median and sum of a numeric column of the Offdayly 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 disclosure burden and does reveal meaningful behavior: commas/currency are parsed and non-numeric rows are excluded and counted. But the meaning of 'counted' is ambiguous (separate count vs included in stats) and there is no mention of empty-column or missing-column behavior or return structure.
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?
One dense sentence front-loads the computed statistics and appends only necessary preprocessing caveats in parentheses. No filler or redundancy.
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 read-only summary tool, the description covers core outputs and data-cleaning behavior. It is slightly incomplete because it doesn't clarify the counting semantics or describe the response shape, but the low complexity makes these gaps non-critical.
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 for the single 'column' parameter. It adds that the column is numeric and that formatted numbers will be parsed, but it doesn't specify exact column-name requirements, case sensitivity, or whether an existing column must be passed. The added semantics are helpful but incomplete.
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?
Description names a specific resource (numeric column of the Offdayly dataset) and enumerates concrete output metrics (count, min, max, mean, median, sum). It lacks an explicit verb like 'computes', but the stat list makes the operation unmistakable and helps distinguish it from siblings like dataset_top or dataset_columns.
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?
Usage context is implied: it is for numeric columns in the Offdayly dataset, and the note about commas/currency suggests it applies to formatted numeric data. However, it never names alternatives or states when NOT to use it (e.g., for non-numeric or top-N analysis), so an agent must infer the decision.
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 on the Offdayly dataset: schema, comparison, metadata, exact lookup, fuzzy search, aggregation, and ranking. There is no overlap or ambiguity between them, so an agent can confidently select the right tool for a given query.
All tools follow the uniform pattern 'dataset_' followed by a single descriptive noun or verb (columns, compare, provenance, row, search, stats, top). This consistent naming convention makes the tool set predictable and easy to navigate.
With 7 tools, the server covers the essential querying needs for a dataset without bloat or missing core functionality. Each tool serves a clear purpose, and the count is well within the ideal range.
The tool set provides comprehensive read-only access to the dataset: schema, metadata, exact and fuzzy search, comparisons, aggregations, and top/bottom ranking. A minor gap is the lack of a direct 'get all rows' or pagination tool, but the existing tools allow agents to retrieve data effectively for most use cases.