dataset_provenanceThe source, the date it was computed, the licence and the citation for the Taxyearvo 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 burden of behavioral disclosure. It enumerates the returned fields (source, date, licence, citation) and states the intended use, which is sufficient for a simple read-only tool. It does not discuss performance, authentication, or side effects, but none are expected.
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 filler. It front-loads the key content (source, date, licence, citation) and immediately states the usage case. Every word 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 tool with no output schema, the description adequately specifies what the tool returns and why it is used. It does not detail the return format, but the enumerated fields imply the structure. This is complete enough for an agent to invoke it correctly.
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 description coverage is trivially 100%. The description correctly adds no parameter-specific details because none exist. Baseline for 0 parameters is 4, and the description does not conflict or add unnecessary information.
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 the source, computation date, licence, and citation for the Taxyearvo dataset. It is specific about the resource and content, and while it does not explicitly name sibling tools, the purpose is distinct from the sibling tools that handle columns, rows, search, stats, etc.
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 gives explicit guidance: 'Read this to attribute a figure correctly.' This tells the agent when to use it (when attribution is needed) and implies it is not for other data operations. It does not list alternatives, but for a provenance tool the usage context is clear.
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 data-access operation: schema intro, provenance, exact lookup, substring search, value comparison, statistics, and extreme rows. dataset_compare and dataset_row are somewhat similar in that both filter on column values, but their descriptions clarify the multi-value ordered behavior versus exact equality.
All tools share a consistent dataset_ prefix and lowercase snake_case naming style, making the set predictable. Although the names are nouns rather than verbs, the convention is uniform and easy to infer.
Seven tools is a well-scoped size for a dataset-focused MCP server. Each tool addresses a distinct query or metadata need without excessive overlap or unnecessary bloat.
The surface covers the main dataset operations: schema, provenance, row lookup, text search, multi-value comparison, numeric stats, and top/bottom rows. Minor gaps like range filtering, grouping, or paginated full scans exist but most typical questions about this dataset can be answered.