dataset_provenanceThe source, the date it was computed, the licence and the citation for the Depreo 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 of behavioral disclosure. It does not explicitly state that the operation is read-only or free of side effects, though that is implied by the nature of a provenance tool. It also does not mention any auth, rate limits, or return format. For a simple metadata fetch, this is adequate but not exhaustive.
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 with no filler. The first sentence front-loads the content (source, date, licence, citation), and the second gives the usage directive. Every word contributes value.
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 zero-parameter tool with no output schema, the description fully covers what the tool does and when to use it. There is no missing information that an agent would need to invoke it correctly.
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 baseline is 4. The description does not need to explain any parameter semantics, and it correctly omits any such details.
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 resource (Depreo dataset provenance) and the specific attributes it provides: source, date computed, licence, and citation. This distinguishes it from sibling tools that deal with data content (columns, search, stats, etc.) rather than metadata. The title reinforces the purpose, but the description adds the concrete items returned.
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 second sentence gives explicit usage context: 'Read this to attribute a figure correctly.' This tells the agent when to invoke it (when attribution is needed). It does not explicitly state when not to use it or name alternatives, but given the sibling set, the purpose is clear enough to route correctly.
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.
Most tools are clearly distinct: schema, provenance, exact row lookup, search, stats, top-N, and comparison each serve different purposes. The only mild ambiguity is between dataset_compare and dataset_row, since both retrieve rows by column values, though descriptions clarify ordered multi-value vs exact single-value use.
All tools share a consistent dataset_ prefix and snake_case convention, making them easy to recognize as part of one set. However, the suffix mixes nouns like columns, row, and stats with verbs like compare and search, so it does not follow a uniform verb_noun pattern.
Seven tools is well-scoped for a dataset querying server. Each tool covers a distinct common operation—schema, provenance, lookup, search, stats, top-N, and comparison—without unnecessary bloat or redundancy.
The tool surface covers the core dataset exploration workflow well: schema, provenance, exact and fuzzy lookup, numeric statistics, top-N ranking, and row comparisons. Minor gaps like arbitrary aggregation or pagination exist, but they are not critical for typical dataset questions.