dataset_statscount, min, max, mean, median and sum of a numeric column of the Nofovo 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?
Given no annotations are provided, the description carries the full transparency burden. It discloses important parsing behavior (grouping commas and currency handled) and data-cleaning behavior (non-numeric rows excluded and counted), which go beyond obvious expectations. It does not detail error or edge-case handling, but for a simple stats tool this is meaningful disclosure.
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 entire description is one compact sentence that leads with the output statistics and adds two key handling caveats. Every word contributes; there is no redundancy or 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?
The description is adequate for summoning the tool but leaves gaps: with no output schema, it does not state the return structure, and it is ambiguous whether 'count' refers to numeric rows only or includes the non-numeric count. Error or empty-column behavior is also unaddressed, which matters because the description is the only source of guidance.
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?
With 0% schema description coverage, the description must explain the lone 'column' parameter, and it does: it must be a numeric column of the Nofovo dataset. It also implies that formatted values (commas, currency) are acceptable. It does not enumerate valid column names, but that is dynamic and presumably discoverable via dataset_columns.
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 lists the exact statistics computed (count, min, max, mean, median, sum) and names the resource (Nofovo dataset), making the purpose unmistakable. This distinguishes it from sibling tools like dataset_search, dataset_top, or dataset_row, none of which produce summary statistics.
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 it clear this is the tool for summary statistics, so usage is strongly implied. However, there is no explicit statement about when to choose this over siblings or what conditions would make a different tool more appropriate; it leaves selection to inference.
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, provenance, stats, top, search, exact-row, and compare all serve different question types. However, dataset_row and dataset_compare both filter by column values and could be confused for single-value lookups, and dataset_search adds a third overlapping retrieval path.
All tools share a consistent dataset_ prefix and use lowercase snake_case, making the set predictable. The second part mixes nouns (columns, provenance, row, stats, top) with verbs (compare, search), so the naming is mostly consistent but not a strict verb_noun pattern.
Seven tools is well-scoped for a dataset query server. Each tool addresses a distinct common question type, and none feel redundant or unnecessary.
The set covers schema discovery, provenance, exact lookups, fuzzy search, comparisons, summary statistics, and extreme values, which handles most dataset question-answering needs. Minor gaps exist around arbitrary inequality filters or combining conditions across multiple columns, but agents can typically work around these.