dataset_statscount, min, max, mean, median and sum of a numeric column of the HardFM 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?
No annotations are present, so the description carries the full burden. It reveals that non-numeric rows are excluded and counted, and that comma/currency formatting is handled. However, it does not mention whether the operation is read-only, error behavior for missing columns, or the exact structure of the returned statistics.
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, concise sentence that lists the statistics and key handling details without unnecessary fluff. It is well-structured and immediately understandable.
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?
There is no output schema, so the description must stand alone. It explains what statistics are computed and how data is preprocessed, but it does not state the format of the returned result (e.g., a JSON object with named fields), which is a notable gap for a tool with no output schema.
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 has full coverage for the single parameter (column) with type and minLength. The description adds that the column must be numeric and implies the parameter refers to a column name, but it does not provide explicit examples or clarify edge cases like column name ambiguity.
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 states the tool computes count, min, max, mean, median, and sum for a numeric column, specifying the resource (HardFM dataset). It is distinguishable from sibling tools like dataset_row or dataset_top, 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 mentions handling of grouping commas/currency and exclusion of non-numeric rows, which provides some usage context. However, it does not explicitly state when to choose this tool over alternatives like dataset_compare or dataset_search, or indicate prerequisites such as data cleaning needs.
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 role—schema, provenance, exact lookup, substring search, value comparison, stats, and top/bottom—so the surface is easy to navigate. The only minor ambiguity is between dataset_row, dataset_search, and dataset_compare, all of which retrieve rows but with different matching semantics.
All tools follow a consistent dataset_ prefix with lowercase snake_case names. The naming is predictable and immediately signals the domain, making it easy for an agent to infer the purpose of any tool.
Seven tools is a well-scoped count for a single-dataset read-only MCP server. Each tool covers a meaningful query mode without unnecessary duplication or bloat.
The toolset covers the main dataset exploration needs: schema discovery, provenance, exact and substring search, multi-value comparison, numeric summaries, and extreme rows. A few advanced workflows—such as arbitrary filtering, grouping, or custom aggregations—are not directly supported, but the provided tools cover most common questions.