trigger_testRun a specific test against a target. Specify either the test definition ID or slug. The test is queued and executed asynchronously — use get_run to check status.
| Name | Required | Description | Default |
|---|---|---|---|
| testId | No | Test definition ID (use this or testSlug) | |
| targetId | Yes | The target ID to test | |
| testSlug | No | Test slug e.g. "security-headers" (use this or testId) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of revealing behavior. It discloses that the test is queued and executed asynchronously and points to get_run for status, which is a key behavioral trait. It could additionally state the return format or error conditions, but the core behavior is clearly communicated.
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 with no filler, immediately stating the action and then the key operational detail. It is well-structured and 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 trigger tool with no output schema, the description covers the main actions, parameter selection, asynchronous execution, and follow-up via get_run. Minor omissions like explicitly stating the required targetId or return value are covered by the schema or implied, making it reasonably complete.
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 already provides full descriptions for all three parameters with 100% coverage. The description's phrase 'Specify either the test definition ID or slug' reinforces the mutual exclusivity already stated in the schema but adds no new semantic nuance.
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 'Run a specific test against a target', identifying the action and resource. It distinguishes from siblings like trigger_all_tests by emphasizing 'specific test', and from get_run by positioning itself as the initiation step.
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: it is for running a single predefined test, and it directs the user to get_run for checking status. It does not explicitly mention alternatives like trigger_all_tests or trigger_journey, but the context is sufficient for typical use.
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 separated by resource (targets, runs, findings, incidents, etc.) and action. A few close pairs like active_runs/list_runs and mute_finding/create_muting_rule could confuse, but descriptions clarify the distinctions.
The majority of tools follow verb_noun naming (create_target, get_target, delete_journey). A few outliers use noun phrases (active_runs, daily_trends, system_health, team_stats) which slightly breaks the pattern, but overall the convention is predictable.
74 tools is extreme for any MCP server. Even for a comprehensive monitoring platform, this overwhelms agents with too many granular operations (e.g., enable_all_tests vs disable_all_tests vs update_test, or import_targets duplicating create_target). A more consolidated set would be appropriate.
The tool surface is remarkably complete for the monitoring domain: full CRUD for targets, journeys, rules, reports, secrets, and fragments; plus run triggering, incident management, findings handling, SEO tracking, guest scans, and admin tools. Only maintenance windows lack an update operation, which is minor.