find_by_licenceReturn all datasets with the given licence, summarised. Use to enumerate what's available under a specific licence for compliance/reuse scoping.
| Name | Required | Description | Default |
|---|---|---|---|
| licence | Yes | Exact licence name or supported alias, e.g. 'Creative Commons Attribution 4.0'. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / licence / examplesAdded value: +[
+ "Creative Commons Attribution 4.0",
+ "CC BY 4.0",
+ "OGL"
+]Input schema / properties / licence / descriptionAdded value: +"Exact licence name or supported alias, e.g. 'Creative Commons Attribution 4.0'."Input schema / properties / licence / minLengthAdded value: +1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds that results are 'summarised' and include 'all' datasets, which clarifies the output scope beyond what annotations provide. No contradiction with annotations.
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, front-loads the core functionality, and includes the use case without any redundant or extraneous information.
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?
Given the simple single-parameter schema, strong annotations, and presence of an output schema, the description sufficiently covers the purpose and use context. It adequately explains what the tool does and when to use it.
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 schema already provides 100% coverage of the single parameter, including description and examples. The tool description does not add additional parameter semantics beyond referencing 'the given licence', so baseline 3 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 verb ('Return'), the resource ('all datasets'), and the scope ('with the given licence, summarised'). It distinguishes itself from siblings by focusing specifically on licence-based enumeration rather than general searching.
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?
It provides an explicit use case ('Use to enumerate what's available under a specific licence for compliance/reuse scoping'), but does not mention when to avoid it or name alternative tools. This is clear context without exclusions.
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 workflow—search, detail retrieval, freshness checking, licence enumeration, and citation metadata. The only mild overlap is between get_dataset and get_provenance, both exposing metadata, but their descriptions differentiate full health/freshness detail from citation-ready provenance.
All tool names follow a clear verb_noun pattern in snake_case: find_, get_, and search_ prefixes are used consistently. Minor stylistic variation between find_by_licence and find_stale does not undermine predictability.
Five tools is well-scoped for a dataset catalog server, covering discovery, inspection, health assessment, licence scoping, and citation. No redundant or excessive tools are present.
The core lifecycle is covered: search to find datasets, get_dataset for full detail, find_stale for freshness risk, and get_provenance for citation. A minor gap is the lack of a general list-all or status filter beyond stale, but the domain is narrow enough to work around this.