List all supermodels (W1 through W19): id, name, role, lane, lead shape, tagline. The supermodel directory is the canonical lineage for every spawned brood variant. Free up to 100 calls/day per agent-DID; $0.001/call thereafter via x402 USDC settlement on Base.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses rate limits and pricing ('Free up to 100 calls/day per agent-DID; $0.001/call thereafter via x402 USDC settlement on Base') and describes the returned field set. It stops short of specifying the exact return format or any pagination behavior, but for a simple list tool the coverage is solid.
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: the first front-loads the action and outputs, the second adds context and cost details. No filler or redundancy.
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 no-parameter, no-output-schema list tool, the description is complete: it names the full set of items, the fields returned, its purpose in the lineage system, and cost/rate-limit constraints. Nothing essential is missing.
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 baseline is 4. The description adds clarity by naming the implicit scope (all supermodels W1–W19), but there are no params to explain.
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' plus the exact resource 'all supermodels (W1 through W19)' and enumerates the returned fields (id, name, role, lane, lead shape, tagline). This clearly distinguishes it from sibling tools like morph_get_supermodel, which likely targets a single supermodel.
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?
It states the directory is 'the canonical lineage for every spawned brood variant,' which gives clear context for when the tool is relevant. However, it does not explicitly name when to prefer this over siblings or list exclusions, so it falls short of a 5.
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.