dataset_statscount, min, max, mean, median and sum of a numeric column of the Footfally 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 meaningfully discloses edge-case handling: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. It does not mention return format or error behavior, but for a simple stats tool these disclosures are strong.
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 compact sentence communicates the output statistics up front, then appends the two important behavioral caveats in parentheses. There is no redundancy or unnecessary detail.
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 single-parameter aggregation tool with no output schema, the description lists the returned statistics and key input nuances, which is largely sufficient. It leaves minor gaps such as exact output formatting or behavior for an invalid or empty column, but these are not critical given the tool's simplicity.
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% (the only parameter 'column' has no description). The description compensates fully by explaining that the column must be numeric, that formatting like commas and currency is accepted, and that non-numeric values are excluded and counted. This adds critical semantics the schema alone lacks.
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 action (compute count, min, max, mean, median, sum) on a clear resource (a numeric column of the Footfally dataset). This is precise and distinguishable from the sibling tools like dataset_top or dataset_columns, which target different operations.
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 implied through the description: use when summary statistics for a numeric column are needed. However, no explicit when-to-use, when-not-to-use, or alternative routing is stated, leaving the agent to infer when this tool is preferable over sibling 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 targets a distinct retrieval mode: schema, provenance, exact match, multi-value comparison, substring search, stats, and top/bottom rows. The main ambiguity is between dataset_row and dataset_compare, since both filter by exact column values, and dataset_columns mentions provenance while a dedicated provenance tool exists.
All tools share the dataset_ prefix and use clear, consistent nouns describing their function: columns, compare, provenance, row, search, stats, top. There is no mixing of naming conventions or vague verb-based names.
Seven tools is well-scoped for a single-dataset exploration server. Each tool has a distinct role and the set feels neither bloated nor thin.
The tool surface covers schema discovery, provenance/attribution, exact lookup, fuzzy search, comparisons, numeric summaries, and top/bottom ordering. A minor gap is the lack of a full-table or sampling tool, but core dataset exploration workflows are well supported.