dataset_provenanceThe source, the date it was computed, the licence and the citation for the Topcoatly 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 and pays it: it discloses what the tool returns (source, date computed, licence, citation). The date-computed detail is useful behavioral context. The tool is inherently a non-destructive metadata lookup, so no side-effect warnings are needed.
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, front-loaded with content, then purpose. Every word earns its place; the title and description work together without redundancy.
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 metadata lookup, the description tells the agent what it will receive and why to call it. It could note read-only security posture explicitly, but that is strongly implied by the name and description.
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, and the description compensates by describing output content rather than inputs. Nothing about parameter semantics is missing.
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 a specific resource (the Topcoatly dataset) and the content returned: source, computation date, licence and citation. The title reinforces this. It doesn't brand itself against a sibling explicitly, but provenance is a distinct operation among the listed sibling tools.
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?
'Read this to attribute a figure correctly' gives an explicit when-to-use context for citation/attribution tasks. It doesn't name exclusions or alternatives, but the sibling set (columns, row, search, stats, top, compare) makes the boundary obvious.
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 query pattern—schema, provenance, exact match, contains search, ordered value comparison, aggregation, and top/bottom ranking. The only mild ambiguity is between dataset_row and dataset_search, but their exact-match versus contains-match descriptions make the boundary clear.
All tools share the consistent dataset_ prefix followed by a clear operation name, and all use lowercase snake_case. The pattern makes the purpose of each tool predictable at a glance.
Seven tools is an appropriate scope for a read-only dataset querying server. Each tool addresses a distinct question type without unnecessary sprawl or redundancy.
The toolkit covers schema discovery, provenance, exact and fuzzy lookup, multi-value comparisons, descriptive statistics, and top/bottom ranking—the core workflows for answering dataset questions. It lacks general range filtering or grouped aggregation, but these are minor gaps for the stated purpose.