dataset_provenanceThe source, the date it was computed, the licence and the citation for the Perdiemo 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 has to carry behavioral disclosure. It communicates that the tool returns provenance metadata and implies a read-only citation lookup, but it does not describe the response format or any operational caveats.
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?
A single, front-loaded sentence covers the tool's content and its intended use. The title adds useful context without redundancy.
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 metadata lookup, the description covers the essential content and the main use case. There is no output schema, so a touch more detail about the exact return shape would make it fully complete, but nothing necessary to invoke the tool is missing.
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?
There are no parameters, so the description does not need to document them. It still adds meaning by specifying what fields the provenance result covers, beyond the empty schema.
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 explicitly names the resource (Perdiemo dataset) and the content it provides (source, computation date, licence, citation). It lacks an explicit verb describing the retrieval, but the title and 'Read this to attribute a figure correctly' make the tool's function clear.
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?
It gives a concrete trigger: call this when a figure needs to be attributed to the Perdiemo dataset. It does not name sibling alternatives or exclusions, but for a zero-parameter provenance tool 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 has a clear purpose: schema, provenance, exact matching, substring search, multi-value comparison, numeric stats, and top/bottom ranking. The only potential confusion is between dataset_row, dataset_search, and dataset_compare, but their differing match semantics (exact single value, contains, and multi-value ordering) are described clearly enough.
All tools share the dataset_ prefix, making the group immediately recognizable and predictable. However, the suffix mix of nouns (columns, provenance, row, stats) and verbs (compare, search) breaks the strict verb_noun convention, though this is a minor deviation given the strong prefix consistency.
Seven tools is ideal for a single-dataset query server—enough to cover exploration, retrieval, and analysis without redundancy. Each tool earns its place, and the count is comfortably within the well-scoped range.
The set covers the full read-only lifecycle of dataset exploration: schema discovery, provenance, exact filtering, search, comparison, statistical summaries, and ranking. There are no obvious dead ends or missing operations for the apparent domain of answering questions about the Perdiemo dataset.