dataset_provenanceThe source, the date it was computed, the licence and the citation for the Tickmarko 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 provided, so description carries the burden. It discloses the kind of data returned (source, date, licence, citation) and implies no side effects since it is informational. Adequate for a read-only provenance 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?
Two concise sentences that cover content and purpose without any redundancy or fluff.
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 provenance lookup, the description lists all the information returned and the context for using it. No output schema exists, but 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 schema has no parameters, so description need not explain them. Baseline 3 applies since there is nothing to add about parameters.
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?
Clearly states the tool provides provenance information: source, date, licence, and citation. This distinguishes it from sibling tools like dataset_columns or dataset_stats which have different purposes.
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?
Explicitly says to read this to attribute a figure correctly, giving a practical use case. Does not explicitly contrast with alternatives, but the purpose is clear enough.
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 inspection, row retrieval, search, comparison, top values, statistics, and provenance. The descriptions make the differences explicit, so an agent can confidently select the right tool.
All tools follow a consistent 'dataset_' prefix with a descriptive noun or verb, such as dataset_columns, dataset_search, dataset_stats. The naming pattern is uniform and predictable.
With 7 tools for exploring a single dataset, the scope is well-balanced. Each tool addresses a specific need without redundancy or bloat, fitting the server's purpose.
The tool surface covers schema discovery, exact lookup, substring search, value comparison, top/bottom extraction, statistical summaries, and provenance—everything needed for read-only dataset exploration. No obvious gaps exist.