dataset_provenanceThe source, the date it was computed, the licence and the citation for the ProbeLedger 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 behavioral burden. It transparently lists what the tool returns (source, computed date, licence, citation) and signals a read-only intent via 'Read this.' There are no hidden side effects or surprising behaviors described, which is acceptable for this simple no-parameter tool.
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 one tightly packed sentence with the most important content front-loaded (source, date, licence, citation) followed by a direct usage instruction. Every word earns its place; there is no fluff 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 zero-parameter metadata retrieval tool with no output schema, the description fully covers what the agent needs to know: what data is returned and why to call it. The sibling context makes the uniqueness of provenance self-evident, so nothing important is missing.
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 the baseline is 4. The description adds no parameter details, but none are needed since the input schema is empty and no input configuration exists.
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 names the specific resource (ProbeLedger dataset) and the exact metadata returned: source, computed date, licence, and citation. It clearly frames the tool as the provenance/citation lookup, which makes its purpose obvious against siblings like dataset_stats or dataset_search, though it does not explicitly call out sibling differentiation.
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 a clear usage directive: 'Read this to attribute a figure correctly.' This tells the agent when to use the tool (when citation or attribution is needed). It does not mention alternatives or exclusions, but for a simple metadata tool the context is sufficient.
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.
Most tools are distinct (schema, provenance, stats, top, compare), but dataset_row, dataset_search, and dataset_compare all return rows with subtly different matching semantics, which could confuse an agent. The descriptions help, but the boundaries between exact match, substring search, and ordered value comparison are not immediately obvious.
All tools share the consistent 'dataset_' prefix and use lowercase snake_case, which creates a clear family identity. However, the suffixes mix nouns (columns, row, stats, provenance) with verbs (compare, search, top), so the pattern is not perfectly uniform.
Seven tools is a well-scoped number for a single-focused dataset exploration server. Each tool addresses a distinct query need without redundancy or bloat.
The toolkit covers schema discovery, statistics, filtering, ranking, comparisons, and provenance, which covers most dataset exploration workflows. A minor gap is the lack of a way to retrieve arbitrary rows or the full dataset without a matching condition.