Kopern

kopern_create_agent

Create a new AI agent by providing a system prompt, model, and optional skills. Returns the unique agent ID for deployment.

Instructions

Create a new AI agent with a system prompt, model, and optional skills. Returns the agentId.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>",
      "system_prompt": "<system_prompt>"
    },
    "name": "kopern_create_agent"
  }
}

Input Schema

Name	Required	Description
`name`	Yes	Agent name
`model`	No	Model ID. Default: claude-sonnet-4-6
`domain`	No	Domain (e.g. 'customer_support', 'coding', 'other'). Default: other
`skills`	No	Optional skills (domain knowledge blocks)
`provider`	No	LLM provider. Default: anthropic
`description`	No	Short description
`builtin_tools`	No	Built-in tools to enable: web_fetch, memory, github_read, github_write, bug_management, datagouv, piste, service_email, service_calendar
`system_prompt`	Yes	The agent's system prompt

Schema Changelog

Changes observed during successful MCP inspections.

Addedv2.0.5Apr 12, 2026

TDQS

A3.8/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already convey that this is a mutating (readOnlyHint=false) and non-idempotent (idempotentHint=false) operation. The description adds useful context by stating it returns the agentId, but does not disclose additional behaviors such as error handling or permission requirements, so it provides only modest added value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that states the action and the key return value. Every word earns its place; there is no waste or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a create tool with no output schema, the description covers the core function, key input types, and the return value (agentId). The parameter details are thoroughly documented in the schema, so the description is sufficiently complete for its complexity, though it could mention error or conflict behavior for full completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all eight parameters. The description mentions system prompt, model, and optional skills, which aligns with the schema but adds no extra semantic detail beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Create'), identifies the resource ('AI agent'), and names key attributes (system prompt, model, optional skills). This clearly distinguishes it from sibling tools like kopern_update_agent, kopern_get_agent, and kopern_delete_agent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage (when you want a new agent) but does not explicitly state when to use it over alternatives or provide exclusions. It's clear but lacks explicit when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.