dataset_statscount, min, max, mean, median and sum of a numeric column of the Coshhvo 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?
There are no annotations, so the description carries the full burden. It discloses non-trivial behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It does not cover error cases or output format, but the core data-handling behavior is clearly communicated.
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 dense sentence with no filler. The output statistics are front-loaded, followed by important data-handling caveats. Every part of the sentence contributes useful information.
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 read-only statistics tool, the description lists all computed values and key data-cleaning behaviors, effectively acting as a return-value specification in the absence of an output schema. Minor gaps such as exact output field names and error behavior do not prevent correct invocation.
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 says column is a string with minLength 1, providing no semantic guidance. The description compensates by clarifying that the column must be numeric and that parsing quirks like commas and currency are managed. For a single simple parameter, this is adequate.
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 operation: it computes count, min, max, mean, median, and sum for a numeric column of the Coshhvo dataset. This distinguishes it from siblings like dataset_search, dataset_row, and dataset_columns, which address different needs.
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 intended use is implied: use when summary statistics for a numeric column are needed. However, it does not explicitly mention alternative tools or exclusion criteria, so the agent must infer when this tool is preferable to siblings like 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 targets a distinct dataset operation: schema, provenance, exact lookup, multi-value comparison, substring search, numeric stats, and ranking. The only near-overlap is dataset_row and dataset_compare, but the multi-value/ordered behavior of compare makes its purpose clearly different.
All tools follow a consistent dataset_<noun> snake_case pattern. The convention makes the tool surface predictable and easy to navigate.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct querying need without the set feeling bloated or sparse.
The tool set covers the full lifecycle of exploring a read-only dataset: schema discovery, provenance, exact filtering, fuzzy search, comparison, statistics, and top/bottom ordering. There are no obvious dead ends for common dataset questions.