dataset_provenanceThe source, the date it was computed, the licence and the citation for the Netsheetly 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?
There are no annotations to indicate read-only or destructive behavior, so the description carries the full burden. The description implies a read-only metadata lookup by naming the provenance fields, but it does not explicitly state that no data is modified, nor does it mention any side effects or error conditions.
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 redundant wording. It front-loads the key content (source, date, license, citation) and ends with a clear purpose statement, making it efficient and easy to parse.
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?
The description names all the key provenance fields an agent would need for attribution and gives a clear context of use. It does not specify the exact return format or citation style, but given the absence of an output schema and the simple nature of the tool, this is adequate.
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 coverage is 100%, so there is nothing for the description to add about parameter meanings. The description focuses on the returned metadata, which is appropriate for a parameterless tool.
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 states the tool provides provenance metadata for the Netsheetly dataset, specifically the source, computed date, license, and citation. This is distinct from sibling tools focused on columns, rows, search, stats, and comparison, making its purpose immediately identifiable.
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 read this tool when attributing a figure, giving a clear use case. It does not explicitly contrast with sibling tools, but the phrase 'Read this to attribute a figure correctly' provides sufficient guidance for when this tool is appropriate.
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 distinct operation: schema, provenance, exact row lookup, substring search, ordered comparison, statistics, and top/bottom extremes. dataset_row and dataset_compare are somewhat similar, but their descriptions make the single-value vs ordered-multi-value distinction clear.
All tools share the consistent dataset_ prefix and snake_case style, making the family recognizable. However, the second segment mixes noun, verb, and adjective forms (columns, compare, top), so the naming is not a strict verb_noun pattern.
Seven tools is well-scoped for a dataset querying server. Each tool covers a different common question type without redundancy, and the count feels neither thin nor bloated.
The tool set covers schema discovery, provenance, exact lookup, substring search, comparisons, summary statistics, and top/bottom ordering. For a read-only dataset QA server, there are no obvious missing operations or dead ends.