dataset_statscount, min, max, mean, median and sum of a numeric column of the TermsBird 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 burden. It discloses that grouping commas and currency are handled, and that non-numeric rows are excluded and counted, which is valuable edge-case context. It also enumerates the exact statistics returned.
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?
A single, well-organized sentence that front-loads the key statistics and then adds parenthetical edge-case handling. Every word contributes value with no repetition or filler.
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 one-parameter aggregation tool with no output schema, the description adequately covers return values, column semantics, and data-cleaning behavior. It does not explicitly state the output response format or behavior for a missing/invalid column, but these are minor gaps for such a simple contract.
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 only defines 'column' as a string with minLength 1, so parameter semantics are otherwise absent. The description adds meaning by clarifying that the column must be numeric, belong to the TermsBird dataset, and may contain formatted values like commas or currency that are normalized.
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 summary statistics (count, min, max, mean, median, sum) for a numeric column of the TermsBird dataset. This clearly differentiates it from siblings like dataset_search, dataset_row, 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 usage by stating it operates on a numeric column, but it provides no explicit when-to-use guidance or comparisons to alternatives among the sibling tools. There are no exclusions, but also no advice on when to pick this tool over dataset_columns or dataset_top.
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 has a clearly distinct purpose: schema discovery, filtering, provenance, exact lookup, substring search, statistics, and top/bottom sorting. No two tools overlap in function, and the descriptions reinforce the boundaries.
All tools follow the consistent dataset_<verb> pattern with no deviations. The naming convention is uniform and predictable.
7 tools is well-scoped for a dataset querying server. Each tool covers a necessary operation without redundancy or bloat.
The tool set covers schema, row retrieval, search, statistics, sorting, comparison, and provenance. The only minor gap is a straightforward 'get all rows' operation, but the existing tools can likely cover most workflows.