dataset_provenanceThe source, the date it was computed, the licence and the citation for the Mustvo 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?
No annotations are provided, so the description carries the full burden. It discloses the type of data returned (source, date, licence, citation) but does not explicitly state that the operation is read-only or non-mutating. While the nature of the tool implies no side effects, it would be safer to state this explicitly. It also doesn't mention any limitations like caching or staleness.
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 concise sentences with no filler. It front-loads the key information (what is returned) and immediately follows with a clear usage instruction. Every sentence 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 zero-parameter metadata retrieval tool with no output schema, the description adequately covers what it returns and when to use it. It could specify the return format more precisely, but for such a simple tool, the description is nearly complete. The sibling tools don't overlap in function, so there's no risk of confusion.
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, and the schema reflects that. Per the rubric, a baseline of 4 applies for 0 parameters, and the description correctly doesn't attempt to explain parameters that don't exist.
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 specific resource (Mustvo dataset) and the information provided (source, date, licence, citation). It distinguishes itself from sibling tools that operate on data content rather than metadata, making its purpose 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 description explicitly states when to use it ('Read this to attribute a figure correctly'), giving a clear usage context. It doesn't mention alternatives or exclusions, but the purpose is so distinct from siblings that this 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.
Most tools target clearly distinct operations: schema, provenance, exact match, substring search, stats, ranking, and comparison. dataset_row and dataset_compare both filter by column values, but their stated purposes (exact lookup vs. ordered multi-value comparison) keep them mostly distinguishable.
All tools share a consistent dataset_ prefix and snake_case style, making the namespace predictable. There is minor mixing between noun-style names (dataset_columns, dataset_row) and verb-style names (dataset_compare, dataset_search), but the overall pattern is still readable.
Seven tools is well-scoped for a single-dataset exploration server. Each tool covers a distinct common query need without unnecessary redundancy.
The surface covers schema discovery, provenance, exact lookups, substring search, numeric summaries, top/bottom ranking, and multi-value comparisons. Minor gaps like combined filters or pagination beyond 50 results exist, but agents can work around them for most questions.