dataset_statscount, min, max, mean, median and sum of a numeric column of the Take-Home Compass 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 two important behavioral details: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This adds valuable context beyond the schema. It does not mention error handling or return format, but for a simple stats tool this is reasonable coverage.
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 list of statistics, then adds necessary caveats about formatting and non-numeric rows. It is concise, with no redundant words or phrases, and every element earns its place.
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 tool with no output schema, the description covers the main behaviors: which statistics are computed, how formatting is handled, and the treatment of non-numeric rows. The missing explicit return format is a minor gap given the simple nature of the tool.
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 description coverage is 0%, so the description must compensate. It clarifies that the 'column' parameter is a column name from the dataset and that it should be numeric. This is essential semantic information that the plain string type in the schema does not provide.
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 that the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Take-Home Compass dataset. It is specific about the resource and the operation, making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like dataset_top or dataset_columns, so it stops short of a 5.
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 gives clear context that this is for numeric columns, implying when to use it. But it does not mention any alternatives or exclusions, so an agent might not know when to prefer dataset_search or dataset_top. It lacks explicit when-not-to-use guidance.
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, exact-row lookup, substring search, multi-value comparison, aggregate stats, top/bottom ranking, and enquiry lifecycle steps. Even the three query tools (dataset_row, dataset_compare, dataset_search) are semantically separate and described with enough precision to avoid misselection.
All tools follow a consistent snake_case pattern with a domain prefix: dataset_* for the data exploration tools and enquiry_* for the form workflow. The suffix is sometimes a noun (columns, provenance, fields) and sometimes a verb (search, compare, submit), but the uniform prefix and predictable structure make the set easy to navigate.
Ten tools is a well-scoped size for a server covering two related areas: dataset analysis and enquiry submission. Each tool earns its place; there is no obvious redundancy or bloat, and the split of seven dataset tools and three enquiry tools matches the apparent purpose.
The dataset tools cover schema discovery, provenance, filtering, searching, comparison, statistics, and ordering—a solid analytical surface. The only notable gap is a way to retrieve all rows at once without a filter, though that may be intentionally omitted since most queries are targeted. The enquiry tools form a complete describe-fields-submit flow.