dataset_provenanceThe source, the date it was computed, the licence and the citation for the Curtilo 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 full burden for behavioral disclosure. It clearly explains what information the tool will return and frames it as a read-only lookup. Although it does not explicitly state 'read-only' or 'no side effects', the zero-parameter nature and descriptive content make the behavior transparent enough.
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 concise sentences with no filler. It front-loads the core content and ends with the practical instruction on when to read it; every sentence 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?
For a zero-parameter metadata lookup with no output schema, the description fully covers what an agent needs: the returned categories and the intended use case. Nothing essential 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 there is nothing to document. The baseline for a parameterless tool is 4, and no additional semantic explanation is needed.
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 returns: source, computed date, licence, and citation for the Curtilo dataset. It also connects the purpose to a concrete use case ('attribute a figure correctly'), and the title reinforces the scope. This clearly distinguishes it from the data-manipulation sibling tools.
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 context: read this when attribution of a figure is needed. It does not explicitly mention alternatives, but with zero parameters and a distinct provenance purpose, the intended use is unambiguous relative to the sibling 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 clearly distinct purpose: schema discovery, provenance, exact row lookup, substring search, category comparison, numeric statistics, and top/bottom ranking. The descriptions make the boundaries between similar tools explicit.
All tools follow the same `dataset_` prefix with descriptive lowercase suffixes. The naming pattern is uniform and predictable, even though the suffixes mix nouns and verbs.
Seven tools is well-scoped for a single-dataset querying server. Each tool covers a distinct query or metadata need without unnecessary bloat.
The tool surface covers schema discovery, provenance attribution, exact lookup, free-text search, multi-value comparison, statistical summaries, and top/bottom ranking. For a read-only dataset server, this is a complete and practical set with no obvious dead ends.