dataset_statscount, min, max, mean, median and sum of a numeric column of the Termslane 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 does add useful behavior details: grouping commas and currency are handled, and non-numeric rows are excluded and counted. However, it leaves ambiguity about the output structure, error handling for missing columns, and precise behavior for empty inputs.
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 the core functionality front-loaded and the edge-case handling in a parenthetical. Every phrase earns its place with no fluff 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?
Given the low complexity (one parameter, no output schema, no annotations), the description covers the main operation and two important behaviors. However, it does not specify the return format, which is arguably needed when there is no output schema, nor does it address failure scenarios like a nonexistent column.
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% and the schema only defines 'column' as a non-empty string. The description compensates somewhat by clarifying that the parameter refers to a numeric column in the Termslane dataset, but it does not explicitly state the expected input format or how column names map to the parameter.
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 resource (numeric column of the Termslane dataset) and the specific statistics computed (count, min, max, mean, median, sum). It implicitly distinguishes itself from siblings like dataset_row and dataset_top by offering aggregate statistics. It lacks an explicit verb like 'computes', which keeps it from a 5, but the meaning is unambiguous.
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?
There is no guidance on when to choose this tool over alternatives or any prerequisites (e.g., column must exist, must be numeric). The description only states what the tool does, leaving the agent to infer suitability from the tool name and sibling context.
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.
Most tools target distinct query types: schema, provenance, exact match, substring search, group comparison, statistics, and top/bottom ranking. dataset_row and dataset_compare overlap somewhat (compare is a multi-value variant of row), and dataset_search could be used for the same purpose, but the descriptions clarify the differences well.
All tools follow a clear dataset_ prefix with snake_case names, making the family instantly recognizable. The second part mixes nouns (columns, row, stats, top) and verbs (compare, search), but the pattern is still predictable and readable.
Seven tools is a well-scoped set for a dataset querying server. Each tool addresses a common question type about the Termslane dataset without unnecessary bloat or missing fundamentals.
The toolset covers schema discovery, provenance, exact lookup, substring search, group comparisons, numeric statistics, and top/bottom ranking — a solid set for answering typical dataset questions. A possible gap is lack of pagination or arbitrary row listing, but the search and row tools cover most practical needs.