dataset_provenanceThe source, the date it was computed, the licence and the citation for the Keysvo 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 provided, the description carries the burden of disclosure. It states what information is returned (source, date, licence, citation) but does not mention output format, pagination, or any side effects (though there are none for a metadata read). It is adequate but not rich.
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 extremely concise—two sentences—and front-loads the key content (what it provides) before the usage hint. There is no redundant or filler wording.
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 no-parameter, no-output-schema metadata tool, the description fully covers what an agent needs: it specifies the exact fields returned and the purpose. Nothing is missing for successful invocation.
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 the description does not need to explain them. Per the rubric, a baseline of 4 is appropriate when no parameters exist, and the description adds no param-related details but also does not need to.
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 the source, computed date, licence, and citation for the Keysvo dataset. This is a specific verb+resource, and it is distinct from siblings like dataset_columns or dataset_stats which focus on other aspects.
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 advises reading this tool 'to attribute a figure correctly', giving a concrete use case. It does not explicitly contrast with alternatives, but given the sibling list, the purpose is unambiguous and the usage context is clear.
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 query mode: schema, metadata, exact match, substring search, multi-value comparison, statistics, and top/bottom ordering. There is minor overlap between dataset_compare and dataset_row since both filter on column values, but their descriptions clearly separate multi-value ordered lookups from single exact matches.
All tools share the consistent dataset_ prefix and snake_case format, which makes the family easy to recognize. However, the suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), so the pattern is consistent in prefix but not perfectly uniform in part of speech.
Seven tools is well-scoped for a dataset query server. Each tool covers a distinct useful operation without redundancy or bloat, and the count feels appropriate for the stated purpose.
The toolset covers the full read-only dataset lifecycle: schema discovery, provenance, exact lookup, fuzzy search, value comparison, numeric statistics, and top/bottom ranking. There are no obvious dead ends for common dataset questions, and no missing operations seem necessary for the domain.