dataset_provenanceThe source, the date it was computed, the licence and the citation for the HeadcountDesk 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 of behavioral disclosure. It states what information is returned (source, date, licence, citation) but does not explicitly confirm it is a read-only operation or describe any side effects. For a metadata tool this is acceptable but not exhaustive.
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 a single, tightly written sentence that front-loads the key facts (source, date, licence, citation) and the intended use (attribute a figure correctly). There is no filler or 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?
There is no output schema, so the description must convey what the agent will receive. It lists the components of the return (source, date, licence, citation), which is sufficient for a simple metadata tool. It does not specify formatting or structure, but that is a minor omission given the simplicity of the tool.
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 schema coverage is 100% by definition. According to the rubric, 0 parameters receives a baseline score of 4. The description adds no parameter information because none is needed; the tool requires no input.
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 what the tool does: it provides the source, date computed, licence, and citation for the HeadcountDesk dataset. This is a specific verb-resource mapping that distinguishes it from siblings like dataset_columns or dataset_stats, which deal with data content rather than provenance.
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 says 'Read this to attribute a figure correctly', giving a clear context for when to use the tool. It does not mention alternatives or when not to use it, but the purpose is so specific that an agent can infer when it applies without confusion.
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 maps to a distinct query type: schema discovery, provenance, exact matches, substring search, value comparison, numeric stats, and top/bottom rows. The potential overlap between dataset_row and dataset_compare is mitigated by the explicit X-vs-Y ordering purpose of compare.
All tools share the consistent dataset_ prefix and snake_case, making the family immediately recognizable. However, suffixes mix nouns (columns, row, stats) with verbs (compare, search), so the pattern is not perfectly uniform.
Seven tools is a well-scoped set for interacting with a single read-only dataset. Each tool covers a distinct user need without redundancy or overwhelming breadth.
The dataset surface is complete for the apparent purpose: schema, provenance, exact lookup, full-text search, comparison, summary statistics, and ranked extremes are all available. No obvious query type relevant to this domain is missing.