dataset_provenanceThe source, the date it was computed, the licence and the citation for the Retainvo 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 full burden. It discloses what information will be returned (source, date, licence, citation) and implies a read-only operation. It does not mention potential errors or performance, but such concerns are minor for a provenance/metadata tool.
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 concise sentences pack all necessary information: the purpose and the use case. The essential content is front-loaded, with no filler or repetition.
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 no-parameter, no-output-schema tool, the description lists the expected returned fields. It explains when to use it (attribution) and what it provides, which is sufficient for an agent to invoke it correctly. Minor gap: no mention of output format, but the listed fields imply a structured result.
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 no parameters (schema coverage 100% vacuously). The description does not need to explain parameters, and the baseline for zero parameters is 4. No additional meaning is required.
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, citation) for the Retainvo dataset. This specific verb+resource is unambiguous and distinct from sibling tools that operate on data rows, columns, 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' explicitly identifies when to use the tool. It implies a distinct use case from data querying, though it does not explicitly name alternatives or exclusion conditions.
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 handle schema, metadata, aggregates, and sorted rows, while row, compare, and search retrieve rows with clearly different matching semantics. Row and compare could be confused (exact vs any-of) but the descriptions make the distinction explicit.
All tools share the 'dataset_' prefix and snake_case, giving a uniform and predictable family. However, the second element mixes nouns (columns, provenance, row, stats) with verbs (compare, search, top), so it is not a strict verb_noun pattern.
Seven tools is within the ideal range and each tool covers a distinct dataset querying capability. There is no redundancy or bloat, and every tool earns its place for the server's narrow purpose.
The set covers schema discovery, provenance attribution, exact and fuzzy row retrieval, numeric statistics, and top-N ranking. For a read-only dataset exploration server, there are no obvious missing operations or dead ends.