dataset_provenanceThe source, the date it was computed, the licence and the citation for the Tenantvo 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. It discloses that the tool returns provenance metadata (source, date, licence, citation) but does not explicitly state it is read-only or non-mutating. While the nature of the tool implies no side effects, the lack of explicit disclosure keeps it at an adequate but not outstanding level.
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 a single, front-loaded sentence that lists exactly what is provided and ends with a clear usage directive. Every word earns its place; there is no fluff or 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 tool with no output schema and no annotations, the description covers the essential information: what data it returns and when to use it. It does not specify the exact return format, but the listed fields give a strong hint. The description is sufficient for an agent to call the tool correctly, though a more explicit return structure would make it fully complete.
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 per the rubric. The description adds no parameter-specific information, but that is unnecessary because there are no parameters to document. The schema is trivially complete with 100% coverage.
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 provides the source, computed date, licence, and citation for the Tenantvo dataset, which is a specific and distinct purpose from its siblings (columns, compare, row, search, stats, top). The verb 'read' and the concrete list of metadata fields make it unambiguous.
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 gives a clear usage context: 'Read this to attribute a figure correctly.' It tells the agent when to use the tool (when attribution is needed) but does not explicitly mention alternatives or exclusions. This meets the 'clear context, no exclusions' level.
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.
The tools are mostly distinct: schema, provenance, exact lookup, substring search, comparison across values, summary statistics, and top/bottom queries each serve a different purpose. There is mild overlap among dataset_row, dataset_compare, and dataset_search since all return matching rows, but their match semantics are clearly described.
All tools share the dataset_ prefix and use short, consistent lowercase names: columns, compare, provenance, row, search, stats, top. The pattern is highly predictable and easy to navigate.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a clear part of the query surface without redundancy or bloat.
For a read-only dataset access server, the surface is complete: schema discovery, provenance/attribution, exact row lookups, substring search, value comparison, summary statistics, and top/bottom ranking. There are no obvious dead ends for common dataset questions.