dataset_provenanceThe source, the date it was computed, the licence and the citation for the HardscapeDesk 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?
The description states what information is returned but does not explicitly mention side effects, return format, or error behavior. With no annotations to fill that gap, the description carries the burden, though the read-only nature is strongly implied.
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 two short sentences, directly naming the returned fields and the intended use. There is no redundant wording 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 no input parameters and no output schema, the description fully covers what the tool provides and why it matters. It gives enough context for an agent to select and invoke the tool correctly.
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 input schema is trivially complete and there is nothing to explain. The baseline of 4 applies because the description adds no parameter-specific meaning where none is needed.
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 returning provenance metadata: source, computation date, licence, and citation. It is unmistakably distinct from sibling tools that focus on data access 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 phrase 'Read this to attribute a figure correctly' gives a direct when-to-use instruction. It does not enumerate alternatives, but given the tool's single-purpose nature and zero parameters, this is sufficient.
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 clearly distinct operation: schema inspection, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom ranking. No two tools appear to serve the same purpose.
All tool names follow the same 'dataset_' prefix pattern followed by a simple noun or operation name (columns, compare, provenance, row, search, stats, top). This is highly consistent and predictable.
Seven tools is a well-scoped set for a single-dataset query server. Each tool covers a distinct need without redundancy or bloat.
The surface covers schema discovery, data retrieval, search, comparison, statistics, extremes, and provenance. Minor gaps exist, such as no explicit way to retrieve all rows or generate distinct-value lists, but core query workflows are well supported.