Run the regression gate on a flow now: compile the current version into a synthetic-caller test, run it against the flow's target, and compare the result to the previous version's baseline. A behavior change that regressed (a pass turning into a fail, or a score drop past the threshold) is caught and blocks the change — continuous integration for your phone-system logic.
| Name | Required | Description | Default |
|---|---|---|---|
| flowId | Yes | The flow id to gate. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears the full burden and does well by disclosing the core behavior: compiling, running a synthetic-caller test, comparing to baseline, catching regressions, and blocking changes. It lacks detail on the exact result format or runtime side effects, but the main traits are transparent.
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 sentences, front-loaded with the primary action "Run the regression gate on a flow now" and then packed with relevant procedural detail. Every sentence earns its place; there is 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 single-parameter tool with no output schema, the description provides substantial process context (compile, run, compare, block) and explains the purpose of the tool. The main gap is that it does not describe what the caller can expect in the response (e.g., pass/fail report), but given the low complexity, this is a minor omission.
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 100% coverage for flowId with a clear description, so the baseline is 3. The tool description adds contextual meaning by explaining what the flow is used for (compiled into a synthetic-caller test) but does not meaningfully enhance the parameter's 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 clearly states the tool's function: "Run the regression gate on a flow now" and details the steps (compile, run, compare). It distinguishes itself from siblings like run_test and diff_flow by explicitly focusing on regression detection and blocking changes, making its unique purpose 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 establishes clear usage context as a continuous-integration regression gate ("continuous integration for your phone-system logic") but does not explicitly name alternatives or state when not to use. It implies usage when checking for regressions, which is sufficient though not exhaustive.
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.
