dataset_provenanceThe source, the date it was computed, the licence and the citation for the Cafmlane 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 behavioral disclosure burden. It does state the returned content (source, date, licence, citation) and implies a read-only action, but it does not describe whether the data is generated on demand, batch-provided, or what the output format looks like. For a zero-parameter metadata tool this is acceptable but minimally transparent.
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, tight sentence that front-loads the most useful information (source, date, licence, citation) and immediately gives the usage context. The title reinforces the purpose without repeating the description verbatim. No wasted words.
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 covers the core purpose, the data elements included, and the intended use case. Given the low complexity and the clear sibling context, nothing essential is missing for an agent to select and invoke this tool 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 and schema coverage is 100% (empty properties object), so there are no parameter semantics to explain. The description appropriately focuses on what provenance information is returned rather than input handling.
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 a specific resource (the Cafmlane dataset) and the purpose: returning provenance metadata (source, computed date, licence, citation) for attribution. The title and first clause together distinguish this from sibling tools that explore columns, rows, stats, or search, since this is the only provenance-specific tool.
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 to use this tool when attribution is needed: 'Read this to attribute a figure correctly.' It implies this is the appropriate tool among the dataset siblings for citation/provenance needs, though it does not explicitly name alternatives or state when not to use it.
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 operation: schema, provenance, exact match, substring search, multi-value comparison, statistics, and ranking. There is some overlap between dataset_row and dataset_compare, but the descriptions clarify single-value vs multi-value use.
All tools follow a consistent dataset_ noun pattern in snake_case. The naming clearly indicates the operation each tool performs.
Seven tools is well-scoped for a dataset exploration server. Each tool earns its place by covering a distinct query mode without unnecessary redundancy.
The set covers schema inspection, provenance, exact filtering, substring search, comparison, summary statistics, and top/bottom ranking. Missing generic list-all or group-by aggregation, but the core analytical workflows are well covered.