dataset_statscount, min, max, mean, median and sum of a numeric column of the Patientvo 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?
Without annotations, the description provides meaningful behavioral details: it mentions that grouping commas and currency are handled, and that non-numeric rows are excluded and counted. This gives an agent insight into data preprocessing and potential edge cases, though it omits broader error conditions or 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 concise and well-structured: it lists the output statistics, specifies the dataset, and notes important data handling details in parentheses. Every sentence adds value, with no redundant or vague wording.
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 a single parameter and a straightforward output, the description covers the core functionality and edge-case handling (currency, non-numeric rows). It does not specify the exact output format or return type, but this is not critical given the absence of an output schema and the simplicity of the task.
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 single parameter 'column' is minimally described in the schema (string, minLength 1). The description clarifies that it refers to a numeric column in the dataset, but does not elaborate on naming conventions, case sensitivity, or what happens if the column does not exist. The added context is modest beyond the 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 clearly states the action (computing summary statistics) and the specific resource (the Patientvo dataset), listing the exact statistics (count, min, max, mean, median, sum) and the target (a numeric column). This unambiguously distinguishes it from sibling tools like dataset_row or dataset_search.
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 it—when summary statistics are needed for a numeric column—and notes that non-numeric rows are handled. It does not explicitly compare against sibling tools, but the distinct purpose makes the appropriate context clear enough for an agent.
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 have clearly distinct purposes: schema, search, exact row lookup, multi-value comparison, stats, top/bottom, and provenance. Some slight overlap exists between dataset_row and dataset_compare for single-value filters, but the descriptions generally make the intended use clear.
All tools share a consistent dataset_ prefix and use snake_case, which makes them predictable. The suffixes are a mix of result nouns and action verbs, but this does not create confusion because the prefix dominates the naming pattern.
Seven tools is a well-scoped set for a single-dataset query server. Each tool serves a distinct analytical need without unnecessary redundancy, making the surface easy to navigate.
The tool set covers schema exploration, provenance, exact lookup, text search, group comparison, summary stats, and top/bottom ranking. Minor gaps like pagination or distinct-value listing are workable around with existing tools, so the core domain is well covered.