Run the auto-cull dry-run scan: which brood variants the system would cull based on conversion floors, ROI thresholds, and saturation. READ-ONLY — does not actually cull.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It explicitly discloses the tool is READ-ONLY and 'does not actually cull,' which is the key safety-relevant behavior. It also outlines the culling criteria, providing useful context. However, it doesn't describe return format or whether repeated runs are side-effect free beyond the no-cull guarantee.
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, then the criteria and the READ-ONLY warning. Every word earns its place, with no redundant or vague phrasing.
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 no parameters and no output schema, the description adequately covers the tool's purpose, criteria, and safety behavior. It explains the outcome in terms of 'which brood variants the system would cull,' which implies the output is the list of variants. The simplicity of the tool means no further detail is needed.
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 tool has zero parameters, so the schema is empty. The description adds meaning by explaining what the scan does and what it evaluates, compensating for the lack of parameter detail. Baseline is 4 for zero params.
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 'Run' and clearly identifies the resource as the 'auto-cull dry-run scan,' explaining its function (which brood variants would be culled) and the criteria (conversion floors, ROI thresholds, saturation). It also distinguishes from a real cull by explicitly stating it's a dry-run and READ-ONLY.
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 implies usage for previewing cull decisions without executing, but it does not explicitly state when to use this tool over siblings like morph_spawn_monitor_scan or any alternatives. The READ-ONLY nature suggests a preview context, but no direct guidance or exclusions are given.
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 target distinct resources (broods, supermodels, money flavor, audit, carousel). The pair morph_money_flavor_probe and morph_money_flavor_stats are close but distinguished by scope (latest window vs rolling stats). Similarly, morph_brood_conversion and morph_brood_conversion_leaderboard are related but serve different purposes. Overall, minimal overlap.
All tools share the 'morph_' prefix, but the structure after is inconsistent: some use verb+noun (morph_get_identity, morph_list_supermodels), others use noun+descriptor (morph_brood_conversion, morph_money_flavor_probe), and some are just nouns (morph_carousel). This mixed convention makes the naming pattern less predictable.
With 14 tools, the count is appropriate for the server's broad scope covering supermodels, broods, money flavor, audit, and scans. Each tool has a distinct purpose, and the number is within the ideal range.
The server provides comprehensive read-only coverage for analytics: listing/fetching supermodels, brood conversion metrics, audit logs, money flavor stats, and dry-run scans. However, it lacks write operations or a way to act on pending approvals, which may be intentional but leaves a gap for full lifecycle management.