Enable the embeddable chat widget for an agent. Returns the embed code for your website.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | The agent ID or name | |
| position | No | Widget position. Default: bottom-right | |
| allowed_origins | No | Allowed website domains (CORS). Empty = all origins. | |
| welcome_message | No | Greeting message shown in widget |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include idempotentHint=true and readOnlyHint=false. The description adds the return of embed code, which is useful, but does not disclose side effects or prerequisites. Given annotations cover the safety profile, this is adequate but not rich.
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 with no filler. The first sentence states the action, the second the return value. Every word earns its place.
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?
Tool complexity is low with 4 parameters and no output schema. The description covers the main functionality but doesn't elaborate on optional parameter effects or return format details. Given output schema absence, slightly more detail could help, but it's sufficient for the scope.
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%, and all parameters have descriptions. The tool description adds no extra parameter detail, so it relies on the schema. 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 action ('Enable the embeddable chat widget') and the specific resource ('for an agent'). It also mentions the output ('Returns the <script> embed code'), making it distinct from sibling tools like Slack or email connectors.
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 for website embedding by mentioning 'your website'. While it doesn't explicitly exclude other connect tools, the widget-specific naming and website context provide clear usage guidance relative to siblings.
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.
