dataset_provenanceThe source, the date it was computed, the licence and the citation for the Dividvo 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 behavioral disclosure burden. It signals a read-only action ('Read this') and lists the exact information returned, including licence and citation. Minor behavioral details like output format or error behavior are not covered, but they are low risk for a zero-parameter 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 a single sentence that names all key deliverables and then gives a practical usage instruction. It is compact, front-loaded, and contains no filler.
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 simple, parameterless provenance tool, the description is complete: it tells the agent what content will be returned, why the tool exists, and when to invoke it. No output schema is needed to understand the basic outcome.
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 and the schema is already complete, so there is nothing for the description to add about parameter semantics. This meets the baseline for parameterless tools.
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 clearly identifies the tool as the source of provenance metadata for the Dividvo dataset, listing exactly what it returns: source, computed date, licence, and citation. This distinguishes it from sibling tools that explore, search, or compare dataset content.
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 states when to use it: 'Read this to attribute a figure correctly.' It doesn't mention alternatives or exclusions, but the purpose is clear enough that an agent can distinguish it from 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 different purpose: schema, provenance, exact match, search, stats, ranking, and comparison. dataset_row and dataset_compare overlap for single-value equality, but the descriptions make the ordered multi-value use case clear.
All tools follow the same dataset_ prefix with a descriptive noun or verb, forming a highly predictable naming pattern. There is no mixing of conventions or vague generic names.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct query mode without redundancy or unnecessary bloat.
The set covers schema discovery, provenance, exact filtering, substring search, numeric statistics, top/bottom ranking, and comparisons. Minor gaps exist such as pagination or listing all rows, but agents can accomplish most dataset tasks with these tools.