dataset_provenanceThe source, the date it was computed, the licence and the citation for the WalkthroughDesk 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, the description carries the burden of conveying that this is informational. The explicit 'Read this' phrasing signals a non-mutating lookup, and listing the exact data fields provides transparency about what will be returned. It doesn't mention side effects, but none are plausible for a provenance metadata 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 two sentences with no filler. The title reinforces the purpose, and the description front-loads the key contents (source, date, licence, citation) before the usage instruction.
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 parameterless metadata lookup, the description fully covers what the tool returns and why to use it. No output schema exists, but the listed fields are enough for the agent to know what to expect. There are no missing prerequisites or edge cases to disclose.
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 schema is fully covered. The description does not need to explain parameters; it meaningfully describes the dataset that the metadata applies to, which is sufficient for an agent to understand the scope of the call.
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 states exactly what the tool provides: source, computed date, licence, and citation for the WalkthroughDesk dataset. It clearly differentiates this from sibling tools like dataset_columns or dataset_stats, which handle data content rather than provenance.
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 instructs the agent to use this tool when attribution is needed: 'Read this to attribute a figure correctly.' It doesn't explicitly name alternatives, but the use case is clear and distinguishes it from the other dataset tools.
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 has a clear role: schema, provenance, exact row lookup, substring search, compare, stats, and top/bottom rows. dataset_row and dataset_compare both filter rows, but the distinction between exact single-value lookup and ordered multi-value comparison is clear enough from the descriptions.
All tools follow a consistent dataset_<noun> pattern, making the tool family immediately recognizable and predictable. No mixed styles or vague verbs are present.
Seven tools is well-scoped for a dataset exploration server: schema, provenance, lookup, search, comparison, stats, and ranking cover the core operations without unnecessary redundancy.
The tool surface covers schema discovery, provenance, exact lookup, substring search, comparison, numeric statistics, and ordering, which covers most common dataset questions. Minor gaps exist such as no general multi-condition filtering or pagination for search results, but agents can work around these.