dataset_statscount, min, max, mean, median and sum of a numeric column of the Fax Line Index 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 burden. It discloses useful behavior: formatted numbers are parsed and non-numeric rows are excluded/counted. It does not specify the output format or how missing/blank values are classified, which leaves part of the behavior undisclosed.
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?
One compact sentence front-loads the returned statistics, then adds parsing and exclusion behavior. No wasted or repeated content beyond the title.
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 single-parameter read-only statistics tool, the description supplies the dataset, the target column type, the computed values, and edge-case handling. It would benefit from stating the return shape, but it is enough for an agent to invoke it correctly.
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 coverage is 0%, so the description is the only semantic source for the single column parameter. It communicates that column must refer to a numeric column of the Fax Line Index dataset and that formatted values are handled; this is adequate, though valid column names are not enumerated.
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 names the exact operation—compute count, min, max, mean, median, and sum—over a numeric column of a specific dataset. This is specific enough to distinguish dataset_stats from the search/row/top/columns siblings.
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 context of use is implied: use it when summary statistics for a numeric column are needed. However, it never states when not to use it or contrasts it with sibling tools such as dataset_top or dataset_search.
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, provenance, search, exact match, stats, top, compare, describe, fields, and submit. No overlap or ambiguity between dataset exploration and enquiry submission tools.
All tools follow a consistent snake_case pattern with clear prefixes (dataset_ and enquiry_) and descriptive suffixes (columns, search, stats, submit). Naming is uniform and predictable.
10 tools is well-scoped for a server that handles dataset exploration (7 tools) and an enquiry workflow (3 tools). Each tool earns its place and covers necessary operations without redundancy.
The dataset tools cover schema, provenance, search, exact match, statistics, top values, and comparison, while the enquiry tools cover description, field schema, and submission with confirmation. No obvious gaps for the stated purpose.