dataset_statscount, min, max, mean, median and sum of a numeric column of the TakeoffDeck 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 important edge-case behavior: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This gives useful insight into how the tool processes messy data, though it does not mention error handling or permissions.
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, focused sentence that conveys all essential information without redundancy. It lists the statistics, names the dataset, and notes data cleaning behavior, making it efficient and easy to parse.
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 lists the exact statistics returned, so the output content is clear. It also covers handling of formatted numbers and non-numeric rows. However, it does not describe the output format (e.g., JSON structure) or potential error conditions like missing columns.
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' has no schema description (0% coverage). The description implies it refers to a column in the TakeoffDeck dataset, but it does not specify whether the column must exist, the exact string format, or whether the column is expected to be numeric (it says 'numeric column' but does not enforce it).
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 that the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column and names the specific dataset. This clearly distinguishes it from other dataset tools by focusing on statistical aggregation.
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 no guidance on when to choose this tool over alternatives such as dataset_columns or dataset_row. It gives no selection criteria or context about which scenarios call for summary statistics versus other operations.
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 querying need: schema, provenance, exact match, substring search, comparison, statistics, and top/bottom ranking. Dataset_row and dataset_compare could be confused for single-value lookups, but their stated purposes (exact equality vs. X/Y comparisons) make them distinguishable.
All tools follow a consistent dataset_ prefix with lowercase snake_case naming. Although the second token mixes nouns and verbs, the pattern is highly predictable and easy to infer.
Seven tools is well-scoped for a read-only dataset querying server. Each tool covers a distinct query mode without unnecessary redundancy or bloat.
For the apparent domain of exploring and querying a single dataset, the surface is complete: schema discovery, provenance, exact lookup, search, comparison, statistics, and ranking are all covered. No obvious read-only query operations are missing.