dataset_provenanceThe source, the date it was computed, the licence and the citation for the Venbix 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 behavioral burden. It clearly indicates this is a read-only informational tool that returns provenance metadata, and implies no side effects or destructive operations. It could explicitly state 'read-only' or describe the return format, but the intent is clear.
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. The content is front-loaded and directly informs an agent about what the tool offers and why it should be used.
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, simple informational tool, the description provides everything needed to call it correctly: what it returns, the dataset it refers to, and when to use it. No output schema is required for this level of simplicity.
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 parameter semantics are not a concern. The description fully explains what the tool returns without needing to compensate for schema gaps.
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 a specific resource (the Venbix dataset) and a clear purpose: retrieving the source, date, licence, and citation. It distinguishes itself from sibling tools by focusing on provenance/metadata rather than data access or manipulation.
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 clear usage context. It does not explicitly list exclusions or alternatives, but the sibling names make the distinction obvious, and the zero-parameter interface makes this tool's use unambiguous.
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.
dataset_row, dataset_search, and dataset_compare all retrieve rows by matching values, so an agent could misselect between exact, substring, and multi-value lookups. The descriptions clarify the matching semantics, and the remaining tools are clearly distinct.
All tools share the dataset_ prefix and consistent snake_case, making the family recognizable and predictable. The second part mixes nouns and verbs, but the shared prefix carries the naming pattern well.
Seven tools is well-scoped for a single-dataset query server; each covers a distinct query mode without unnecessary bloat. It sits comfortably in the ideal 3–15 range.
The toolkit covers the full exploration lifecycle: schema discovery, provenance/attribution, exact lookup, substring search, multi-value comparison, numeric statistics, and top/bottom ranking. No major dead ends are apparent for the stated purpose of answering questions about the Venbix dataset.