dataset_provenanceThe source, the date it was computed, the licence and the citation for the Capanix 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 behavioral disclosure burden. It enumerates exactly what information will be returned and implies a read-only metadata lookup through 'Read this'. It does not specify output formatting or whether the provenance is static, but for a zero-parameter metadata tool the disclosure is adequate.
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, useful sentences: the first states the contents of the provenance information, and the second states the intended use. There is no redundant or filler language.
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 simple zero-parameter, no-output-schema tool, this description is complete. It tells the agent what data is returned, why to call the tool, and the context in which it is appropriate, without needing more detail.
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 an empty schema, so there is no parameter semantics to explain. The description correctly focuses on what the tool returns rather than how to configure it.
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 tool as the source for provenance information—source, computation date, licence, and citation—and ties it to the concrete task of attributing a figure. This is distinct from sibling tools like dataset_columns or dataset_search, which operate on data content rather than origin.
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 explicitly says to use this tool to attribute a figure correctly, giving a clear trigger for when it is relevant. It does not mention exclusions or alternative tools, but the provenance/citation purpose is naturally disjoint from the data-access-oriented sibling tools.
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 distinct operation in principle, but dataset_row and dataset_compare both filter by column values and could be confused for single-value queries. Overall, search, stats, top, columns, and provenance are clearly separated.
All tools share the consistent dataset_ prefix and snake_case style, which aids recognition. However, the second part mixes verb forms (compare, search) with noun forms (columns, provenance, row, stats, top), so the pattern is not perfectly uniform.
Seven tools is well-scoped for a read-only dataset exploration server. Each tool addresses a distinct common need: schema discovery, provenance, exact lookup, free-text search, comparison, statistics, and top/bottom ranking.
The tool surface covers the full range of expected dataset queries: understanding the schema, retrieving exact rows, searching, comparing values, computing statistics, ranking, and properly attributing the data. No critical operation appears to be missing for the stated purpose.