dataset_provenanceThe source, the date it was computed, the licence and the citation for the Focusvo 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 carries the behavioral disclosure burden. It reveals the tool is a read-only metadata lookup by listing exactly what it returns and using 'Read this'. For a zero-parameter provenance tool, there are no hidden side effects or complex behaviors requiring further 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 description is two compact sentences with no filler. The first sentence front-loads the returned metadata fields, and the second provides the practical use case, making it easy for an agent to scan and act on.
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 provenance tool with no output schema, the description fully conveys what the agent receives, why it exists, and when to use it. No additional details about authorization, side effects, or return formatting are necessary for correct invocation.
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 empty schema is complete and there is nothing for the description to explain about inputs. The description appropriately focuses on the output content instead.
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 identifies what the tool provides: source, computation date, licence, and citation for the Focusvo dataset. 'Read this to attribute a figure correctly' gives it a concrete purpose that distinguishes it from sibling tools focused on columns, rows, stats, or 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 states the intended use case—consult this tool before attributing a figure. It does not explicitly name alternative tools or exclusion conditions, but the context is clear because none of the siblings address provenance or citation.
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 specific purpose: schema, provenance, exact lookup, multi-value comparison, search, stats, and top-N. dataset_compare and dataset_row overlap somewhat since both filter rows by column values, but the multi-value ordered behavior of dataset_compare and the exact single-value behavior of dataset_row keep them mostly distinct.
All tool names follow the same dataset_<noun> pattern, making the set predictable and easy to navigate. The naming clearly signals that every tool operates on the same Focusvo dataset.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct mode of querying or metadata retrieval without redundancy or unnecessary bloat.
The set covers schema discovery, provenance, exact lookup, search, comparison, statistics, and top-N queries, which are the core operations for exploring a dataset. Minor gaps exist such as distinct-value enumeration or grouped aggregation, but agents can work around them with existing tools.