Connect an agent to Gmail or Outlook for email tools (read_emails, send_email, reply_email). Requires OAuth in browser. Enables the service_email builtin tool.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | The agent ID or name | |
| provider | Yes | Email provider |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include only readOnlyHint=false, so the description adds valuable behavioral context: it 'Requires OAuth in browser' (user interaction) and 'Enables the service_email builtin tool' (side effect). This goes beyond the annotation and sets expectations for the user.
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 short sentences, front-loaded with the core action and purpose. Each sentence contributes meaningful information (providers, prerequisite, outcome) with no fluff or repetition.
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 2-param tool with no output schema, the description covers the what, the requirement (OAuth), and the result (enables service_email). It could add details about return values or failure behavior, but the essential context for use is provided.
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% with both agent_id and provider fully described in the schema. The description adds marginal value by matching 'Gmail or Outlook' to provider values, but it does not enrich parameter meanings 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 clearly identifies the action ('Connect an agent to Gmail or Outlook'), the resource (agent), and the purpose (email tools). It also distinguishes from siblings by explicitly naming the email providers and related tools (read_emails, send_email, reply_email), making it unique among the connect_* siblings.
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 situational use by saying 'for email tools' and 'Enables the service_email builtin tool', giving clear context. It does not explicitly list exclusions or alternatives, but the sibling names (calendar, slack, etc.) make the choice obvious.
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.
