dataset_provenanceThe source, the date it was computed, the licence and the citation for the Retainvia 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 supplied, the description carries the behavioral disclosure burden. It discloses the returned content (source, date, licence, citation) and implies a read-only action through 'Read this'. For a zero-parameter provenance tool, this is adequate, although it doesn't explicitly state that no data is modified.
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?
Two short sentences, each earning its place: the first defines the scope and content, the second states the intended use. No filler or repetition.
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 simple, parameterless provenance lookup, the description is complete: it names the dataset, the exact metadata fields, and the user scenario. No output schema exists, but the listed fields serve as sufficient expectations of the return value.
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 input schema is empty with zero parameters, so there are no parameter semantics to document. The baseline for no-parameter tools is 4, and the description adds no unnecessary parameter details.
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 identifies the tool as providing provenance metadata for the Retainvia dataset: source, computed date, licence, and citation. The title reinforces this and the content distinguishes it from sibling tools that operate on columns, rows, stats, or comparisons.
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 phrase 'Read this to attribute a figure correctly' gives a concrete when-to-use trigger: whenever attribution is needed. It does not name sibling alternatives or exclusions, but the described purpose is sufficiently distinct that no further routing is needed.
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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Most tools have distinct purposes, but dataset_row, dataset_search, and dataset_compare all retrieve rows via value matching, creating possible misselection. The descriptions help clarify exact vs. contains vs. list comparison, but boundaries are not crisp.
All tools share a consistent dataset_ prefix and snake_case formatting, making the group recognizable. However, the suffix mixes nouns (columns, row, stats, provenance) with verbs (compare, search, top), so the pattern is not fully uniform.
Seven tools is a well-scoped set for exploring and querying a single dataset. Each tool covers a distinct need without bloat or redundancy at the set level.
The toolset covers schema inspection, provenance, stats, exact lookup, substring search, comparisons, and top/bottom ordering, which forms a solid read-only exploration surface. Minor gaps exist, such as multi-condition filtering or grouped aggregations, but agents can likely work around them.