dataset_provenanceThe source, the date it was computed, the licence and the citation for the DoorsetBook dataset. Read this to attribute a figure correctly.
| 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 provided, the description carries the full burden of behavioral disclosure. It is transparent about what the tool returns: source, computed date, licence, and citation. It implies a read-only metadata operation by saying 'Read this,' and no side effects are suggested. It could mention output format, but the content disclosure is sufficient for this simple tool.
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 two sentences with no wasted words. The first sentence enumerates the key content fields, and the second gives the practical use case. The most important information is front-loaded.
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 zero-parameter tool whose output is a small set of provenance metadata fields, the description is complete. It tells the agent what data is returned and why to use the tool. No output schema exists, but the description explicitly lists the return fields, so an agent knows what to expect.
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 input schema has zero parameters, so there is nothing for the description to explain about parameter usage. Schema coverage is 100% by default. The description correctly focuses on behavior rather than parameters, matching the baseline expectation.
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 identifies the specific resource (DoorsetBook dataset provenance) and states exactly what information is provided: source, computation date, licence, and citation. This clearly distinguishes it from sibling tools like dataset_row or dataset_search, which operate on data content rather than metadata.
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 a clear usage context: 'Read this to attribute a figure correctly.' This tells the agent when the tool is appropriate. It does not explicitly name alternative tools or state when not to use it, but the unique provenance purpose makes the intended usage unambiguous.
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.