dataset_provenanceThe source, the date it was computed, the licence and the citation for the RemitDeck 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 full burden of behavioral disclosure. It openly lists what the tool returns (source, date, licence, citation) and its intended purpose. It does not explicitly state it is read-only, but the lack of parameters and the metadata nature of the tool make side effects highly unlikely. This is reasonably 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?
Two sentences with no fluff. The first sentence front-loads the tool's content, the second delivers the usage guidance. Every word earns its place.
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, no-output-schema tool, the description is complete: it names the returned fields and tells the agent why to call it. An agent can correctly invoke the tool and understand its result without any further documentation. There is no missing information that would prevent correct usage.
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 input schema has zero parameters, so there are no parameter semantics to document. According to the baseline for 0 parameters, the description is not required to compensate. It adds value by describing the output content, which is more meaningful than the empty schema.
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 what the tool provides — source, computation date, licence, citation — and names the specific resource (RemitDeck dataset). It lacks an explicit verb like 'get' or 'return', but the title and phrasing make the purpose unambiguous. It also distinguishes from sibling tools by focusing on provenance instead of data structure or statistics.
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, 'Read this to attribute a figure correctly,' gives a clear, specific use case. However, it does not explicitly mention when not to use it or name alternative tools, though the sibling names imply alternatives. The context is sufficient for an agent to decide when to invoke it.
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.
dataset_row, dataset_search, and dataset_compare all return rows and can overlap when querying by a simple value, but each has a distinct mode: exact match, substring, and multi-value ordered comparison. Schema and provenance tools are clearly separated from row-level queries.
All tools use the same dataset_ prefix and snake_case style, creating a predictable and recognizable family. The second element mixes nouns and verbs, but the pattern is consistent enough to cause no confusion.
Seven tools is well-scoped for a read-only dataset exploration server. Each tool covers a distinct need (schema, provenance, filtering, comparison, statistics, ranking) without unnecessary bloat.
The surface covers schema discovery, provenance, exact/substring filtering, multi-value comparison, numeric stats, and top/bottom ranking. Missing features like group-by or distinct-value queries are minor gaps that can often be worked around with existing tools.