Connect an agent to Google Calendar or Microsoft Calendar for scheduling tools (list_events, create_event, etc.). Requires OAuth in browser. Enables the service_calendar builtin tool.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | The agent ID or name | |
| provider | Yes | Calendar provider |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the minimal annotation (readOnlyHint:false), the description adds valuable behavioral context: it requires OAuth in a browser and enables the service_calendar builtin tool. This conveys a mutating, state-changing operation, though it does not discuss reversibility or credential handling, so not a 5.
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 consists of three short, purposeful sentences: the first states the main action, the second gives a critical requirement, and the third explains the result. There is no redundant or extraneous information; every sentence earns its place and is front-loaded.
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 simple tool with only 2 required parameters and no output schema, the description covers all key aspects: what it does (connects agent to calendar), the prerequisite (OAuth), and the expected effect (enables the service_calendar builtin tool). It is complete given the tool's simplicity.
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?
The schema description coverage is 100%, with both parameters (agent_id and provider) already documented in the schema. The description does not add additional meaning beyond what the schema provides, such as examples or constraints, so the baseline of 3 applies.
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 ('Connect an agent') and the target resource ('Google Calendar or Microsoft Calendar'), with the purpose 'for scheduling tools'. It distinguishes itself from sibling connect tools by specifically naming calendar providers and the service_calendar builtin tool, making its unique role unambiguous.
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 provides clear context on when to use the tool (for enabling scheduling tools) and a key prerequisite (OAuth in browser). It also explains the outcome (enables the service_calendar builtin tool), but it does not explicitly mention alternatives or exclusions, so it stops short of a 5.
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.
