list_findingsList findings (discovered issues) across your targets. Filter by status, severity, or target. Returns title, severity, status, test name, and AI enrichment if available.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max number of findings to return | |
| status | No | Filter by finding status (default: OPEN) | |
| severity | No | Filter by severity | |
| targetId | No | Filter by target ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral transparency burden. It discloses the return fields and that AI enrichment is included 'if available', a useful caveat. However, it does not mention defaults like status=OPEN or limit=50, nor explicitly state it's a read-only operation, leaving some behaviors implicit.
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 action and resource, and every phrase adds value. No redundancy or fluff.
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 no output schema, the description lists the key return fields and filter options. For a simple list tool with fully documented parameters, this is reasonably complete. It could clarify default behavior or the meaning of 'AI enrichment', but overall it covers the essential aspects.
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%, so the baseline is 3. The description mentions filterable dimensions (status, severity, target) which map to parameters but adds no extra meaning beyond the schema's own descriptions. No additional syntax or format detail is provided.
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') and resource ('findings'), clarified as 'discovered issues', distinguishing it from sibling list tools like list_incidents and list_targets. It immediately conveys the tool's scope and purpose.
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 states the tool lists findings 'across your targets' and supports filtering by status, severity, or target. This provides clear context for when to use it, though it does not explicitly mention alternatives or exclusions (e.g., use get_finding for a single finding).
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.