dataset_provenanceThe source, the date it was computed, the licence and the citation for the Lanyardo 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, but the description implies a read-only operation (retrieving metadata). There is no mention of side effects, destructive actions, or authentication requirements. Since the tool simply returns provenance information, the risk is low, but the lack of explicit statement about non-destructiveness leaves a minor gap.
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 concise, using one sentence to list the key elements and another to give a practical use case. No redundant words or unnecessary details. The structure is clear and front-loaded with the core content.
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 tool that returns metadata, the description is complete enough: it tells what to expect (source, date, licence, citation) and when to use it (attribution). It does not specify the exact output format or whether it returns a single object or list, but that is likely self-evident for a provenance tool. Overall, adequate for the task.
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 parameter behavior. The schema coverage is 100% (empty schema), and the description focuses on the output semantics. Given no parameters, a baseline score of 4 is appropriate.
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 source, computation date, licence, and citation for the Lanyardo dataset. This distinguishes it from sibling tools like dataset_row or dataset_stats, which focus on data content or statistics. The title reinforces the purpose of provenance and citation.
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 includes an explicit usage hint: 'Read this to attribute a figure correctly.' This tells the agent when to use the tool (when citation or attribution is needed). However, it does not explicitly mention when not to use it or compare with alternatives, but the context of siblings makes this 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 has a clearly distinct purpose: schema inspection, exact match lookup, substring search, comparison of multiple values, stats computation, top/bottom ranking, and provenance metadata. There is no ambiguity about when to use which tool.
All tools follow a uniform 'dataset_' prefix followed by a descriptive noun or verb (columns, compare, provenance, row, search, stats, top). The naming pattern is consistent and predictable.
Seven tools is well-scoped for a dataset querying server. Each tool covers a distinct operation without redundancy, and the count feels neither sparse nor bloated.
The surface covers schema discovery, data retrieval via exact match, substring search, multi-value comparison, numeric statistics, top/bottom ranking, and provenance. For a read-only dataset server, this is a complete set with no obvious gaps.