dataset_statscount, min, max, mean, median and sum of a numeric column of the Tenantvo 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?
No annotations are provided, so the description carries the full burden. It discloses that grouping commas and currency are handled, and non-numeric rows are excluded and counted, which adds behavioral context beyond the schema. However, it does not specify the output format or error behavior, leaving some gaps.
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, dense sentence that front-loads the list of statistics and includes important parsing details. Every phrase contributes, making it concise and 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?
Given the lack of an output schema, the description should clarify the return format, such as a dictionary of statistics. It lists the stats but does not explicitly state the return structure or how the excluded count is represented. This is a notable gap for 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 schema has one parameter 'column' with 0% description coverage. The description adds meaning by indicating it is a numeric column name from the Tenantvo dataset, but it does not elaborate on accepted formats or behavior for missing columns. It partially compensates but could be more explicit.
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 explicitly states the tool computes count, min, max, mean, median, and sum for a numeric column of a specific dataset. It also mentions handling of formatting and non-numeric rows, which distinguishes it from sibling tools like dataset_row or dataset_top. This is a clear, specific purpose.
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 provides context (numeric column stats) but does not explicitly state when to use this tool versus alternatives like dataset_columns or dataset_row. It implies usage for summary statistics but lacks explicit guidance on when not to use it or which sibling to choose instead.
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 tools are mostly distinct: schema, provenance, exact lookup, substring search, comparison across values, summary statistics, and top/bottom queries each serve a different purpose. There is mild overlap among dataset_row, dataset_compare, and dataset_search since all return matching rows, but their match semantics are clearly described.
All tools share the dataset_ prefix and use short, consistent lowercase names: columns, compare, provenance, row, search, stats, top. The pattern is highly predictable and easy to navigate.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a clear part of the query surface without redundancy or bloat.
For a read-only dataset access server, the surface is complete: schema discovery, provenance/attribution, exact row lookups, substring search, value comparison, summary statistics, and top/bottom ranking. There are no obvious dead ends for common dataset questions.