dataset_provenanceThe source, the date it was computed, the licence and the citation for the Disclovo 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 disclosure burden. It reveals exactly what data the call returns and signals a read-only, side-effect-free operation via 'Read this.' It does not describe the return format, but for a zero-parameter metadata lookup that is a minor gap.
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?
Two short sentences, front-loaded with the content, then the use case. No filler and no repetition of schema information.
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 provenance tool with no output schema, the description covers all essential information: source, date, licence, and citation. Nothing required for correct invocation 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 input schema has zero properties, and the description confirms there is nothing to configure. Baseline for a parameterless tool is 4; no additional parameter semantics are 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 the exact resource (Disclovo dataset) and the exact contents returned (source, computed date, licence, citation). The title reinforces the purpose, and the sibling list makes it clear this is the provenance endpoint rather than a data-access tool. 'Read this to attribute a figure correctly' supplies the intended action.
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 tells the agent to use this tool when attribution is needed. It does not name alternatives or exclusion conditions, but none of the sibling tools handles provenance, so the guidance is still adequate.
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 clearly distinct operation: schema introspection, provenance, exact row lookup, substring search, multi-value comparison, statistics, and top/bottom ranking. Despite some overlap among row, compare, and search, the descriptions make the boundaries obvious.
All tools follow the same dataset_ prefix and use short, readable operation names. The naming is uniform and predictable, making it easy for an agent to infer the purpose of each tool.
Seven tools is a well-scoped size for a dataset Q&A server. Each tool provides a distinct capability without unnecessary duplication, and the count is appropriate for the domain.
The tool set covers the core dataset workflow: schema discovery, provenance, row retrieval, search, comparison, statistics, and ranking. Minor gaps exist such as multi-column filtering or distinct-value extraction, but these are not major blockers for typical questions.