dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the DoorsetBook dataset. Call this first to learn the schema.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 what the operation returns (column metadata, row count, provenance banner) and implies a read-only schema inspection. It does not explicitly state whether it has side effects or how the provenance banner is represented, leaving some behavior unspecified.
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 brief—a list of returned content plus a one-sentence usage instruction. Both sentences are informative with no filler. The opening is a noun phrase rather than an imperative verb, which slightly weakens structural front-loading but does not waste 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?
For a parameterless tool with no output schema or annotations, the description covers the main return values but leaves some context ambiguous, such as the exact meaning of 'provenance banner' when a sibling tool dataset_provenance exists, and the response format. It is adequate for deciding to call it first but not fully self-contained.
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 tool takes zero parameters, and the schema is fully covered by the empty input schema. The description does not need to explain parameters; the baseline for a parameterless tool is met, and the description adds clarity about the output rather than parameters.
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 specifies the resource (DoorsetBook dataset) and the exact pieces of information returned (columns, numeric flags, row count, provenance banner), and states the intent: 'Call this first to learn the schema.' It does not explicitly contrast with sibling tools, but the primary purpose is unambiguous.
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 instruction 'Call this first to learn the schema' provides clear guidance on when to invoke this tool relative to others, establishing a first-step ordering. It does not name alternatives or exclusions, so it stops short of a full when-to-use/when-not-to-use explanation.
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 distinct primary purpose—schema, provenance, exact lookup, multi-value comparison, search, stats, and top rows. The main ambiguity is between dataset_row and dataset_compare, since both retrieve rows by column value, though dataset_compare is specifically for ordered multi-value comparisons.
All tools share the consistent dataset_ prefix and snake_case naming, making them easy to group. However, the suffix style is mixed: some are nouns (columns, row, stats), some are verbs (compare, search), and one is an adjective (top), which is a minor deviation from a fully uniform verb_noun pattern.
Seven tools is well-scoped for a dataset query server. Each tool covers a meaningful query need—schema discovery, provenance, exact lookup, comparison, search, statistics, and ranking—without unnecessary bloat or missing core access patterns.
The tool surface covers the main ways to interact with the DoorsetBook dataset: schema, metadata, exact and fuzzy lookup, comparisons, aggregates, and top/bottom rows. Minor gaps exist, such as no explicit pagination for large result sets or numeric range filtering, but common questions are well supported.