Connect an agent to WhatsApp Business. Requires Meta Cloud API credentials.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | The agent ID or name | |
| access_token | Yes | WhatsApp Cloud API access token | |
| phone_number | No | Display phone number (optional) | |
| verify_token | No | Webhook verify token (optional) | |
| phone_number_id | Yes | WhatsApp phone number ID (from Meta dashboard) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide idempotentHint=true and readOnlyHint=false, so some behavioral context is already given. The description adds the credential requirement but does not disclose details such as whether an existing connection is overwritten, side effects like webhook setup, or what the return value indicates. Some value beyond annotations, but limited.
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 sentences, front-loaded with the primary action, and no redundant information. 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?
Given the low complexity and full parameter descriptions, the tool is fairly complete. The idempotency annotation adds a useful safety signal. However, it lacks any mention of effects (e.g., notifications, webhook setup) or return values, which could matter for an integration tool. Still, for a simple connector, this is adequate.
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 descriptions cover 100% of parameters, so the baseline is 3. The description mentions credentials, which loosely aligns with access_token and phone_number_id, but does not offer any additional parameter-level meaning beyond the schema.
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 states a specific verb 'Connect' and a clear resource 'agent to WhatsApp Business', distinguishing it from sibling connect tools like Slack or Telegram. The purpose is immediately obvious.
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 clearly implies use for WhatsApp connectivity, but does not explicitly state when to choose this tool over alternatives like other connect tools. It mentions a prerequisite (Meta Cloud API credentials) but lacks comparative guidance.
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.
