dataset_statscount, min, max, mean, median and sum of a numeric column of the Keysvo 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?
The description discloses handling of grouping commas, currency, and exclusion of non-numeric rows, which provides some behavioral insight. However, without annotations, it lacks details on edge cases, errors, or operation side effects.
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 with a parenthetical for additional notes. It avoids unnecessary wording and is well-structured.
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?
The description covers the core functionality and important data handling, but it does not specify the return format, error handling, or behavior when the column is entirely non-numeric. For a simple tool, this may suffice, but more detail would improve completeness.
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 only parameter 'column' is described only by context ('a numeric column of the Keysvo dataset'). No explicit format (name vs. index) or constraints are given, and the schema has no description.
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's purpose: computing count, min, max, mean, median, and sum for a numeric column. It distinguishes itself from sibling tools like dataset_row or dataset_top by focusing on statistical summaries.
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 does not explicitly state when to use this tool compared to alternatives. It implies numeric summary usage but provides no comparative guidance against 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 query mode: schema, metadata, exact match, substring search, multi-value comparison, statistics, and top/bottom ordering. There is minor overlap between dataset_compare and dataset_row since both filter on column values, but their descriptions clearly separate multi-value ordered lookups from single exact matches.
All tools share the consistent dataset_ prefix and snake_case format, which makes the family easy to recognize. However, the suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), so the pattern is consistent in prefix but not perfectly uniform in part of speech.
Seven tools is well-scoped for a dataset query server. Each tool covers a distinct useful operation without redundancy or bloat, and the count feels appropriate for the stated purpose.
The toolset covers the full read-only dataset lifecycle: schema discovery, provenance, exact lookup, fuzzy search, value comparison, numeric statistics, and top/bottom ranking. There are no obvious dead ends for common dataset questions, and no missing operations seem necessary for the domain.