dataset_provenanceThe source, the date it was computed, the licence and the citation for the TimeCardBook 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 full burden. It implies a read-only, informational tool, but does not explicitly state that it has no side effects or that it requires no special permissions. 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?
Two short, direct sentences with no filler. The primary message (what the tool provides) is front-loaded, and the use case is given in the second sentence.
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?
Given the absence of parameters and output schema, the description is sufficient to understand the tool's purpose and expected return value. It could have mentioned that it is metadata-only, but that is not necessary for a clear understanding.
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?
There are no parameters, and the schema coverage is 100% (empty schema). The description explains what the tool returns (source, date, licence, citation), so there is nothing missing in parameter guidance.
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 a specific verb ('read this to attribute') and a specific resource ('TimeCardBook dataset'), and clearly differentiates from siblings by focusing on provenance (source, date, licence, citation) rather than data content or stats.
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 tells when to use it ('Read this to attribute a figure correctly'), giving a clear use case. It does not explicitly contrast with alternatives, but the context and dataset name make the purpose obvious.
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 addresses a distinct query pattern: schema discovery, provenance, exact-match lookup, substring search, ordered multi-value comparison, column statistics, and top/bottom row ranking. The only close pair is dataset_row and dataset_compare, but their descriptions clearly separate exact single-value equality from ordered value-list comparison.
All seven tools use the same dataset_ prefix and snake_case convention, producing a predictable and scannable set. Although suffixes mix nouns (columns, stats) and verbs (compare, search), the consistent prefix and clear semantic labels make naming highly regular.
Seven tools is well-scoped for read-only interrogation of a single dataset, covering metadata, lookup, search, comparison, statistics, and extreme values without redundancy. The count is comfortably in the ideal range for this purpose.
The surface covers the main dataset operations: schema, provenance, exact and fuzzy retrieval, comparisons, aggregates, and ranking. A minor gap is the lack of a way to retrieve all rows or list unique categorical values, but most realistic questions can be answered with the provided patterns.