dataset_statscount, min, max, mean, median and sum of a numeric column of the RemitDeck 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 behavioral burden. It discloses meaningful behaviors: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This adds useful context beyond the title, though it does not describe what happens if the column is missing or contains no numeric values.
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 sentence that front-loads the primary function and key statistics, then appends edge-case handling in a parenthetical. Every clause adds useful information with no 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 simple one-parameter tool with no output schema, the description covers the operation, the returned statistics, and data-cleaning behavior. It is adequate for invocation, though a note about invalid or all-empty column behavior would make it fully complete.
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, and schema description coverage is 0%. The description clarifies that the parameter refers to a numeric column of the RemitDeck dataset, but it does not specify column-name syntax, case sensitivity, or identifier requirements. It provides partial compensation for the undocumented 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 explicitly names the verb (computes summary statistics), the resource (numeric column of the RemitDeck dataset), and the exact statistics produced: count, min, max, mean, median, and sum. This distinguishes it from siblings like dataset_row, dataset_search, and 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 when to use the tool: when summary statistics for a numeric column are needed. However, it does not explicitly state when not to use it or name alternative tools, leaving the agent to infer selection from the sibling list.
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.
dataset_row, dataset_search, and dataset_compare all return rows and can overlap when querying by a simple value, but each has a distinct mode: exact match, substring, and multi-value ordered comparison. Schema and provenance tools are clearly separated from row-level queries.
All tools use the same dataset_ prefix and snake_case style, creating a predictable and recognizable family. The second element mixes nouns and verbs, but the pattern is consistent enough to cause no confusion.
Seven tools is well-scoped for a read-only dataset exploration server. Each tool covers a distinct need (schema, provenance, filtering, comparison, statistics, ranking) without unnecessary bloat.
The surface covers schema discovery, provenance, exact/substring filtering, multi-value comparison, numeric stats, and top/bottom ranking. Missing features like group-by or distinct-value queries are minor gaps that can often be worked around with existing tools.