dataset_statscount, min, max, mean, median and sum of a numeric column of the Endpointo 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. It discloses handling of grouping commas, currency, and exclusion/counting of non-numeric rows, which is useful. However, it does not state whether the operation is read-only, what the output format is, or how errors are handled.
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 sentence that front-loads the list of statistics and immediately defines the scope. Every word earns its place, with no redundancy or 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 simple one-parameter tool, it covers key behaviors including data cleaning and the computed statistics. However, without an output schema, it does not specify the exact response structure (e.g., whether the excluded row count is included in the output), leaving a minor gap.
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 clarifies the column must be numeric and that formatting is handled, adding meaning beyond the schema's type and minLength. However, it lacks examples or specifics about column name conventions.
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 summary statistics (count, min, max, mean, median, sum) for a numeric column of the Endpointo dataset. This specific verb and resource set it apart from sibling tools like dataset_row (row retrieval) and dataset_search (search).
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 implies usage for obtaining numeric column statistics but does not explicitly contrast with alternatives like dataset_top or dataset_compare. It provides context about handling grouping commas and currency, but no when-not guidance or mention of other tools.
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 clearly distinct purpose: schema, exact match, search, comparison, stats, top values, and provenance. No two tools overlap in functionality; an agent can easily select the right tool based on the query type.
All tool names follow the consistent 'dataset_' prefix followed by a descriptive noun (columns, compare, provenance, row, search, stats, top). The naming pattern is uniform and predictable.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a specific aspect of querying and understanding the dataset without unnecessary redundancy or bloat.
The surface covers schema, metadata, exact lookup, substring search, comparison, statistics, and ordering. It lacks features like distinct value enumeration or grouping, but for a read-only dataset exploration tool, the coverage is strong with only minor potential gaps.