dataset_statscount, min, max, mean, median and sum of a numeric column of the Dsarvo 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 full behavioral disclosure burden. It adds meaningful context: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond the schema and helps set expectations about data cleaning. It does not cover error behavior or return format, but for a read-only stats tool this is solid.
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, focused sentence that front-loads the statistics list and then adds relevant edge-case behavior in a parenthetical. Every word contributes value, with 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 tool with one parameter, no annotations, and no output schema, the description is largely complete: it names the target column type, lists the exact returned statistics, and discloses handling of non-numeric and formatted values. It lacks explicit usage guidance and error-handling details, but these are not critical for a simple stats operation.
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 input schema only defines 'column' as a string with minLength 1 and no description (0% schema coverage). The description compensates by clarifying that the parameter is a numeric column of the Dsarvo dataset and hints that non-numeric rows are tolerated. For a single parameter, this adds substantial meaning beyond the bare schema.
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 states a specific operation (computing count, min, max, mean, median, and sum) on a specific resource (a numeric column of the Dsarvo dataset). This clearly differentiates it from siblings like dataset_columns 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 implies when to use the tool: when summary statistics for a numeric column are needed. However, it does not explicitly mention alternatives, exclusions, or conditions that would route an agent to another tool. With sibling names available and unmentioned, the guidance remains implicit rather than explicit.
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.
The metadata and numeric tools (columns, provenance, stats, top) are clearly distinct, but dataset_row, dataset_search, and dataset_compare all retrieve rows by value, so an agent could initially confuse exact-match, contains-search, and multi-value ordering. The descriptions do clarify the boundaries, making the overlap manageable.
All tools share the consistent dataset_ prefix and lowercase style, but the second part mixes nouns (row, columns, provenance, stats) with verbs (compare, search, top). This is predictable enough, though not a strict verb_noun pattern.
Seven tools is well-scoped for a single-dataset server: schema discovery, provenance, three retrieval modes, statistics, and ordering each earn their place without bloat or thinness.
The tool surface covers the main workflows: learn the schema, attribute correctly, look up exact/contains/multi-value rows, compute numeric summaries, and find top/low values. No obvious dead-end operations are missing for the apparent purpose.