dataset_provenanceThe source, the date it was computed, the licence and the citation for the Consentvia 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 burden of behavioral disclosure. It makes clear this is a read-only informational tool returning citation and licensing metadata, and there is no hint of side effects or mutations. It could add output format detail, but the behavior is simple and well conveyed.
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 sentences with no wasted words. It front-loads the specific contents of the provenance record and then states the practical purpose.
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 with no output schema, the description fully conveys what the agent will receive and why to invoke it. Nothing essential is missing.
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 schema coverage is 100%, so there are no parameter semantics to explain. The baseline of 4 applies because no compensation 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 information—source, computation date, licence, and citation—for the Consentvia dataset. This distinguishes it from sibling tools that handle columns, rows, search, stats, and comparisons.
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 use case: read this to attribute a figure correctly. It does not explicitly mention alternatives or when not to use the tool, but for a zero-parameter provenance tool the context 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.
Most tools are clearly distinct: schema, provenance, search, stats, and top each target a different operation. The only ambiguity is between dataset_row and dataset_compare, which both filter rows by column values, though compare explicitly handles multiple values and ordering.
All tools share a consistent dataset_ prefix and use short, descriptive nouns or verbs that align with their function. The naming pattern is uniform and predictable, making it easy to infer what each tool does.
Seven tools is well-scoped for a read-only dataset querying server. Each tool covers a distinct useful query pattern without unnecessary redundancy or bloat.
The server covers the core dataset exploration lifecycle: schema inspection, provenance, exact lookups, multi-value comparisons, text search, numeric statistics, and top/bottom sorting. Minor gaps exist such as pagination or arbitrary multi-condition filtering, but most common questions about the dataset can be answered.