dataset_statscount, min, max, mean, median and sum of a numeric column of the Sittingly 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?
No annotations are provided, so the description carries the full burden. It discloses that grouping commas and currency are handled and non-numeric rows are excluded and counted, which is useful. However, it does not state whether it is read-only, what the output format is, or any error behavior. It gives some behavior but not comprehensive.
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 lists the statistics and then adds handling details. It is efficient and front-loaded with the key information, though it is slightly long. No wasted words.
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?
Given the simplicity of the tool (one parameter) and lack of output schema, the description covers the essential functionality. It mentions the dataset and the statistics computed, and the handling of non-numeric rows. However, it does not specify the exact output structure, which might be needed. It is adequate but not exhaustive.
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 specifies the parameter as a string with minLength 1, with zero description coverage. The description adds meaning by indicating the column must be numeric, which is essential. It could be more explicit about the column name format, but it provides necessary context.
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 tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Sittingly dataset. It distinguishes from siblings like dataset_row or dataset_search by its specific purpose, with a specific verb and resource.
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 makes clear what the tool does but does not provide explicit guidance on when to use it versus siblings. There is no mention of alternatives or conditions. An agent might infer to use it when summary statistics are needed, but it lacks explicit routing.
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.
Most tools have clearly distinct purposes: schema discovery, provenance, exact lookup, substring search, stats, and top/bottom ranking are easy to tell apart. However, dataset_row and dataset_compare overlap in that both do exact column-value matching, with compare merely extending row to multiple values, which could cause some selection uncertainty.
All tools share the clean dataset_ prefix and use lowercase snake_case, making the set feel cohesive. The second part mixes nouns (columns, provenance, row, stats, top) with verbs (compare, search), but the pattern is still predictable and readable.
Seven tools is a well-scoped size for a dataset-querying server. Each tool covers a distinct query need without redundancy or bloat, and the count is comfortably within the ideal range.
The server covers the full read-only lifecycle of working with a dataset: schema discovery, provenance attribution, exact lookup, substring search, comparisons, numeric stats, and top/bottom ranking. There are no obvious dead ends for the stated purpose.