Import an agent from a Kopern export JSON. Creates a new agent with all skills, tools, extensions, and grading suites. No LLM cost.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | The full Kopern agent export JSON (from kopern_export_agent) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate a write operation (readOnlyHint=false) and non-idempotent (idempotentHint=false). The description adds behavior beyond annotations: it creates a new agent with all associated components and states 'No LLM cost', which is useful operational context. There is 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-loaded with the action ('Import'), and every phrase adds value: what it imports, what it creates, and the cost implication. No wasted words.
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 single-param tool with a well-defined input, the description covers the purpose, scope, and a key side-effect (no LLM cost). It does not mention return values or failure modes, but given the simplicity and clear annotation of safety profile, it is adequately complete.
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 description coverage is 100%, with the param 'data' described as 'The full Kopern agent export JSON (from kopern_export_agent)'. The description adds little beyond what the schema already provides, merely restating 'Kopern export JSON'. Since the schema fully documents the parameter, the baseline of 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 'Import' and the resource 'agent from a Kopern export JSON', and specifies it 'Creates a new agent with all skills, tools, extensions, and grading suites.' This distinguishes it from siblings like kopern_create_agent (which likely creates from scratch) and kopern_export_agent (the inverse operation).
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 implies usage when you have a Kopern export JSON, and the schema adds 'from kopern_export_agent', making the context clear. It does not explicitly exclude alternatives like creating a new agent from scratch, so it falls short of a 5, but the intended use case is unambiguous.
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 have clearly distinct purposes (create vs list vs get vs run vs connect). A few potential overlaps exist (deploy_template vs create_agent, import_agent vs create_agent, grade_prompt vs run_grading) but descriptions clarify the differences.
All tools share the 'kopern_' prefix and mostly follow a verb_noun pattern (create_*, get_*, list_*, run_*, connect_*). The exception is 'kopern_compliance_report', which uses a noun phrase without a verb, breaking the otherwise consistent naming.
With 31 tools, this exceeds the 25-tool threshold for well-scoped servers. While the domain is broad (agent lifecycle, grading, pipelines, teams, connectors), the sheer number of tools feels heavy and could be consolidated (e.g., a single 'manage_memory' tool already bundles multiple actions).
Core agent management (create, read, update, delete, list) is solid, and grading has suite creation, execution, and results. However, pipelines and teams lack get/update/delete operations, connectors only support connect (no disconnect), and there's no way to manage grading suites beyond creation and running. This leaves notable gaps for secondary resources.
