dataset_provenanceThe source, the date it was computed, the licence and the citation for the Deflectvo 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 disclosure. It states the output content (source, date, licence, citation) and implies a read-only operation. It does not explicitly confirm non-mutating behavior, but for a provenance metadata tool this is reasonable and clearly non-destructive.
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. The key facts (source, date, licence, citation) are front-loaded, and the usage note follows. Every sentence earns its place.
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 simple metadata tool with no parameters and no output schema, the description fully covers what an agent needs: what the tool returns and why to use it. No missing prerequisites or side effects need disclosure.
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 the schema is empty (100% coverage by definition). Baseline for 0 parameters is 4. The description adds no parameter information because none exist, which is appropriate.
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 returns provenance metadata: source, date computed, licence, and citation. It also gives the purpose (correctly attributing a figure). This distinguishes it from sibling tools that operate on data rows, columns, and 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 description gives a clear usage context: read this to attribute a figure correctly. It implicitly tells the agent when to use it (when citation/provenance is needed) but does not explicitly mention alternatives or exclusion criteria. Since the tool is unique in purpose, 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.
Tools are mostly distinct: columns, provenance, stats, and top are clearly separate. There is some overlap between dataset_compare, dataset_row, and dataset_search for retrieving rows, but the descriptions clarify exact vs substring vs multi-value filtering, reducing confusion.
All tools share the 'dataset_' prefix, providing strong consistency. The second part mixes nouns (columns, provenance, row, stats, top) and verbs (compare, search), which is a minor deviation but still predictable and readable.
With 7 tools, the server is well-scoped for a dataset querying purpose. Each tool addresses a distinct query pattern (schema, provenance, search, exact match, comparison, stats, top/bottom) without redundancy or bloat.
The tool surface covers the main query types needed for exploring a dataset: schema, provenance, search, filter, stats, and ranking. Missing a 'list all' or 'distinct values' tool, but for typical analytical questions the coverage is strong and no dead ends are apparent.