dataset_provenanceThe source, the date it was computed, the licence and the citation for the Attestvio 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, the description must carry the burden of behavioral disclosure. It does disclose the returned content (source, date, licence, citation), but it does not explicitly state that the tool is read-only or has no side effects. For a simple metadata retrieval tool, this is a minor but present gap.
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 content is front-loaded (what data it provides) followed by the practical usage instruction. Every part 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?
The tool is simple with no parameters and no output schema. The description lists the key fields that the output will contain (source, date, licence, citation), which is sufficient for an agent to understand the return value. A slight addition about the output format would make it fully complete, but it is not necessary for this simple case.
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 has zero parameters, so the description cannot be expected to document parameters. The baseline for zero-parameter tools is 4, and the description stays at that level without adding unnecessary parameter-related content.
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 provides provenance metadata: source, date computed, licence, and citation. It also gives a specific use case ('Read this to attribute a figure correctly'), which distinguishes it from sibling tools that handle columns, rows, stats, and search.
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 provides a clear context for when to use the tool: to attribute a figure correctly. It does not explicitly name alternatives or exclusions, but none of the sibling tools serve the same provenance purpose, so the guidance is sufficient.
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 targets a distinct operation: schema, provenance, exact lookup, substring search, multi-value comparison, aggregation, and top/bottom ranking. The only possible confusion is between dataset_row and dataset_compare, since both do exact value matching, but compare's ordering/multiple-value purpose is sufficiently clarified.
All tools share the consistent dataset_ prefix and use lowercase snake_case, which makes them easy to group. The second token mixes nouns (columns, row, stats, top, provenance) with verbs (compare, search), so it is not a uniform verb_noun pattern but remains predictable.
Seven tools is well-scoped for a dataset querying server: every tool covers a distinct aspect of data exploration without redundancy. The count is neither too thin nor too heavy for the stated purpose.
The surface covers the main lifecycle of dataset exploration: schema, provenance, exact/match lookups, search, comparison, stats, and ranking. Minor gaps such as pagination beyond 50 results or arbitrary multi-column filters would improve completeness but are not fatal.