Probe the money-flavor classifier on the most recent settlement window: asset class histogram, chain class histogram, flow class histogram, arb-high count, refused count, non-USDC share.
| 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 full burden. It discloses the output fields and mentions the 'most recent settlement window' context, but it does not explicitly state whether the operation is read-only or if there are side effects, permissions, or rate limits. 'Probe' implies read-only, but it is not explicit.
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 a single, front-loaded sentence that immediately states the action and resource, then enumerates the specific components returned. Every word earns its place with no 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 zero-parameter tool with no output schema, the description is complete. It clearly states the time window ('most recent settlement window') and exactly what histograms and counts are produced, giving the agent a full picture of what to expect.
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 does not need to explain any parameters; it focuses on the operation and output, which is appropriate.
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 states a specific verb ('probe') and resource ('money-flavor classifier') and lists concrete outputs. It is clear, though it does not explicitly differentiate from the sibling morph_money_flavor_stats, so it misses a point for sibling distinction.
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 by describing what it does (probing the most recent settlement window), but it provides no explicit guidance on when to use this tool versus alternatives like morph_money_flavor_stats. No when/when-not or exclusions are mentioned.
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.