Machine Hazard Index reading for a payment rail, facilitator, or endpoint, with component letter-grades and abstention status. [free] (free while the MHI methodology stamps its output pre-calibration; moves to $0.05 on a calibrated release (SPEC §5.3))
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| scope | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations to lean on, so the description carries the behavioral burden. It communicates a read-like operation and usefully exposes pricing/versioning context: free pre-calibration, moving to $0.05 after calibration. It does not discuss auth, rate limits, or failure modes, but those are less critical for this simple read.
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 purpose is front-loaded and the description is compact. The only minor issue is redundancy: '[free]' is immediately followed by a parenthetical explaining the free state and future pricing, which could be trimmed slightly.
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 that there is no output schema, the description usefully tells the agent what kind of result to expect — component letter-grades and abstention status — along with pricing context. It is complete enough for a two-parameter read tool, though an example of the 'id' parameter would strengthen it.
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 description maps the 'scope' enum values to meaningful real-world objects — 'payment rail, facilitator, or endpoint' — which adds semantic value. However, the meaning of the 'id' parameter is left implicit: an agent must infer that it identifies the selected rail/facilitator/endpoint. With 0 percent schema coverage, this gap is noticeable.
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 action and resource: 'Machine Hazard Index reading for a payment rail, facilitator, or endpoint.' It also names concrete outputs (component letter-grades, abstention status), which distinguishes it from broader or sibling tools like get_risk_snapshot and get_alerts.
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 implies when to use it — when an MHI reading for a rail, facilitator, or endpoint is needed — but it provides no explicit guidance about when not to use it or which sibling tool to choose instead. This is adequate context but not a clear usage policy.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct concern: counterparty pre-audit, alerts, machine hazard index, market regime, and composite snapshot. There is no meaningful overlap; even similar risk metrics are clearly differentiated by scope (regime vs. snapshot).
All tools follow a verb_noun pattern with snake_case, mostly using the verb 'get' (get_alerts, get_mhi, get_regime, get_risk_snapshot). One tool uses 'check_counterparty' instead, a minor deviation that does not significantly harm predictability.
Five tools is well-scoped for a risk oracle server. Each tool provides a distinct piece of risk information without redundancy or bloat, fitting firmly in the ideal 3-15 range.
The tool surface covers the primary read operations for a risk oracle: counterparty checks, alerts, hazard index, regime, and composite scores. Minor gaps exist, such as historical trend retrieval or detailed dimension breakdowns per endpoint, but the core workflows are supported.