dataset_provenanceThe source, the date it was computed, the licence and the citation for the Exitvo 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 provided, the description carries the full burden. It discloses the type of information returned (source, date, licence, citation), which is transparent about the output content. It does not mention side effects, but as a read-only metadata tool, that is expected. No contradictions with annotations (none exist).
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. The key information (source, date, licence, citation) is front-loaded, followed by the usage context. It is appropriately concise and 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?
Given that the tool has no parameters and no output schema, the description covers all necessary aspects: what it returns and when to use it. It is complete for an agent to decide 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?
There are zero parameters, so the description correctly omits parameter details. The schema coverage is 100% (vacuously true with no params), and the baseline for zero parameters is 4, which is appropriate.
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's function: providing the source, date, licence, and citation for the Exitvo dataset. It uses a specific verb ('read') and resource, and is distinctly different from siblings like dataset_columns, dataset_search, etc., which handle different aspects of the dataset.
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 provides an explicit usage instruction: 'Read this to attribute a figure correctly.' This tells the agent when to use it (when attribution is needed). It does not explicitly mention when not to use other tools, but the sibling context makes the distinction clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool has a distinct role: schema, provenance, exact row lookup, substring search, multi-value comparison, numeric stats, and top/bottom rows. The only minor overlap is between dataset_row and dataset_compare, but their descriptions clearly separate single-exact-match from multiple-value in-order filtering.
All tools share the consistent 'dataset_' prefix with short, readable suffixes. Most suffixes are nouns (columns, row, stats, top), while 'compare' and 'search' read as verbs, a small grammatical deviation from an otherwise uniform pattern.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a meaningful query operation—metadata, lookup, search, aggregation, sorting—without redundancy or bloat.
The surface covers core dataset workflows: understanding schema, citing provenance, finding rows by exact match or substring, comparing values, computing statistics, and identifying extremes. A minor gap is the lack of distinct-value listing or pagination, but the main use cases are well supported.