dataset_provenanceThe source, the date it was computed, the licence and the citation for the Scopedly 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 enumerates the output fields (source, date, licence, citation) but does not explicitly state that the operation is read-only or has no side effects. For a simple provenance getter, this is acceptable but not fully transparent.
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 highly concise, containing only two short sentences. The main purpose is front-loaded in the first sentence, and the second provides a direct usage instruction. Every word contributes value, with no redundancy or unnecessary detail.
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 an output schema, the description adequately informs the agent of what the tool returns (source, date, licence, citation). It also gives a clear context for when to use it (attributing a figure). No missing information prevents correct invocation or interpretation.
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 description coverage is 100% (no parameters to document). According to the rubric, a baseline score of 4 applies for no parameters. The description does not need to add parameter details, and it does not, so no deduction is made.
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's purpose: to provide provenance information (source, date, licence, citation) for the Scopedly dataset. It uses an instructive phrase 'Read this' and explicitly distinguishes itself from sibling tools by focusing on attribution, making it easy for an agent to select it for that need.
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 provides explicit when-to-use guidance: 'Read this to attribute a figure correctly.' While it does not name alternative tools, the context of sibling tools (e.g., dataset_columns, dataset_stats) implies that this tool is for provenance rather than data manipulation, giving adequate direction.
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 dataset operation, though dataset_row, dataset_search, and dataset_compare all retrieve rows via different matching semantics. The descriptions clearly separate exact equality, substring containment, and multi-value ordering, so an agent can select correctly.
All tool names follow a consistent dataset_<noun> pattern with snake_case throughout. The naming makes the tool family immediately recognizable and predictable.
Seven tools is a well-scoped set for a dataset exploration server. Each tool covers a necessary operation—schema, provenance, lookup, search, comparison, stats, and top rows—without redundancy.
The set covers the core dataset exploration lifecycle well: schema discovery, provenance, exact and fuzzy retrieval, statistics, and ranking. A minor gap is the lack of a general sample/random row tool, but existing operations are sufficient for most dataset questions.