dataset_provenanceThe source, the date it was computed, the licence and the citation for the Dispatchzo 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, the description carries the burden of explaining behavior. It effectively discloses the informational content returned, but it does not explicitly state read-only behavior, output format, or any side effects. For a simple metadata tool this is adequate but not fully 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 short sentences deliver the full purpose with no filler. The most important content—source, date, licence, citation—is front-loaded.
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 metadata tool, the description names the exact items returned and explains when to use it. It could be slightly richer by noting the output format, but nothing critical is missing for tool selection.
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 schema already fully covers the input space. The baseline of 4 applies because no parameter documentation 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 states exactly what the tool provides: source, computation date, licence, and citation for the Dispatchzo dataset. It is clearly distinct from sibling tools like dataset_search or dataset_stats, which focus on data access rather than provenance.
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 does not explicitly name alternatives or when-not-to-use cases, but no sibling tool serves this provenance/citation purpose.
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 need: schema, provenance, exact row lookup, substring search, multi-value ordered comparison, numeric stats, and top/bottom ranking. There is no meaningful overlap that would make an agent uncertain which tool to call.
All tools follow a consistent `dataset_` prefix with concise snake_case names that clearly reflect their function. The naming convention is uniform and predictable across the entire set.
Seven tools is a well-scoped set for a dataset-querying server. Each tool covers a distinct operation and none are redundant or superfluous.
The toolset covers schema discovery, provenance, exact and substring search, comparison, statistics, and ranking—strong coverage for a dataset Q&A server. Minor gaps exist such as no direct way to fetch all rows, list distinct values, or filter on multiple columns simultaneously, but these can usually be worked around with the existing tools.