get_research_productFetch a single research product (publication / dataset / software) by OpenAIRE ID — use after search_publications / search_datasets / search_software to get the full record.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | OpenAIRE id (e.g. "doi_dedup___::abc...") |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Input schema / examplesAdded value: +[
+ {
+ "id": "doi_dedup___::10.1038/nature12373"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "A single research product (publication, dataset, or software)",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it is a safe read. The description adds that it returns the full record and requires an OpenAIRE ID, which is valuable beyond the 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?
A single sentence that conveys all necessary information without verbosity. It could be split for readability, but it remains clear and efficient.
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 tool with one parameter, an output schema, and rich annotations, the description is fully adequate. It explains the purpose, usage sequence, and what to expect (full record).
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?
Schema coverage is 100% and the parameter 'id' is well-described with an example. The description does not add new semantic details beyond the schema, 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 specifies the verb 'Fetch', the resource 'single research product', and lists the product types (publication/dataset/software). It also distinguishes itself from sibling search tools by stating it is used after search to get the full record.
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 explicitly says 'use after search_publications / search_datasets / search_software to get the full record', providing clear context for when to invoke this tool versus searching. It does not list negative cases but adequately guides the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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