Read account-wide testing trends over a window: pass-rate, average score, and run-to-run regressions per suite, plus overall totals. Use this to spot behaviour drift in the voice agents you test.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Look-back window in days (default 90). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It states 'Read' which implies read-only, and lists what the tool returns. However, it does not disclose limitations, potential edge cases (e.g., empty results), or any operational implications. It provides basic transparency but not deeper behavioral context.
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 pack a clear purpose, output summary, and a use case. There is zero redundancy and the key information is front-loaded. Every sentence 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?
Despite having no output schema, the description explicitly lists return contents (pass-rate, average score, regressions, totals). With only one optional parameter already documented in the schema, the description fully covers the tool's simple scope and context.
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%: the sole parameter 'days' is fully described with min, max, and default. The description's phrase 'over a window' aligns with the parameter but adds no extra meaning beyond the schema. Baseline 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 uses a specific verb 'Read' plus a clear resource: 'account-wide testing trends over a window'. It enumerates the output (pass-rate, average score, run-to-run regressions per suite, overall totals) and gives a concrete use case, distinguishing it from sibling tools like get_monitor_health.
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 explicitly says 'Use this to spot behaviour drift in the voice agents you test', which provides clear when-to-use context. It does not mention alternatives or exclusions, but the use case is sufficiently specific to guide selection.
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.
