List your production monitors and their current health (unknown | healthy | degraded | critical). A monitor watches ONE live production line: you stream it completed calls, and it scores each with the same versioned judge that scores your tests, tracks a rolling baseline, and alerts when quality drifts. Requires a pro or enterprise plan.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains what monitors are (watches one live production line, streams calls, scores with a versioned judge, tracks rolling baseline, alerts on drift), and states the plan requirement. This provides useful context beyond a simple 'list' statement, though it does not detail return format or error behavior.
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 structured with the core statement first, followed by explanatory context. The second sentence is somewhat long but informative, and the third states a requirement. Every part earns its place, but the middle sentence could be tightened without losing significant value.
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 that the tool has no parameters, no output schema, and a simple listing function, the description is complete enough. It defines the health states, explains the monitor concept, and notes the plan requirement. No additional details are necessary for correct selection and invocation.
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 input schema has zero parameters, so the baseline is 4. The description adds no parameter information, but none is needed. It is sufficient for a no-argument list operation.
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 uses a specific verb 'List' with a clear resource ('your production monitors') and outcome ('their current health'), including the possible health values. This clearly distinguishes it from sibling tools like get_monitor_health, which presumably targets a single monitor.
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 gives clear context for when to use this tool (to list all production monitors with health status) and includes a prerequisite (requires pro or enterprise plan), but it does not explicitly mention alternatives or when not to use it. The distinction from get_monitor_health is implied but not directly stated.
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 are clearly distinct by resource (monitors, suites, flows, numbers, recordings), but run_test and test_flow could be confused since both execute tests, though their scopes differ. The descriptions help disambiguate them.
All tool names follow a consistent verb_noun snake_case pattern (create_, get_, list_, run_, verify_, etc.), with no camelCase or mixed conventions. Even compound names like get_monitor_health and verify_number_confirm remain predictable.
At 16 tools, the set is slightly above the optimal 3-15 range, but the breadth of the voice-agent testing/monitoring domain justifies each tool's existence. It feels well-scoped rather than bloated.
The tool set lacks update/delete operations for most entities (monitors, suites, flows) and omits a get_run tool to retrieve individual live test results, leaving significant gaps that agents cannot work around. This will cause failures in lifecycle management and live-run result retrieval.
