Returns a 0..100 risk score plus LOW/MODERATE/ELEVATED/HIGH risk band for an AI agent / wallet, with the underlying signals (coverage status, daily spend, denied claim ratio). Free.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | Agent / wallet address (0x...) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It clearly states the return value (score, band, signals) and that the tool is free, which implies a read-only operation without side effects. It does not detail error cases or rate limits, but for a straightforward getter, the disclosure is reasonably transparent.
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 sentence that front-loads the primary output and includes key details (score range, risk band, signal components, free pricing) without any wasted words. It is concise and well-structured.
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 the tool's simplicity (one param, no output schema), the description is sufficiently complete: it explains the return value in actionable detail (score, band, signals) and indicates cost. It does not need to elaborate further for an agent to select and invoke the tool correctly.
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 input schema already provides a description for agent_id with 100% coverage, so the description adds no additional parameter semantics beyond confirming the tool pertains to an AI agent/wallet. This matches the baseline for high schema coverage, where the schema does the heavy lifting.
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 clearly states the tool returns a 0..100 risk score and risk band for an AI agent/wallet, along with underlying signals. This specific verb-resource-output combination distinguishes it from sibling tools like get_reputation or check_agent_insurance, making the purpose unambiguous.
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 implicitly suggests usage through its focus on risk scoring and the note that it is 'Free', which may prompt selection over paid or alternative tools. However, it does not explicitly state when to use this tool versus others, nor does it mention any exclusions or prerequisites beyond the agent_id parameter.
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.
Many tools have similar purposes, such as the three simulation tools (simulate_action, simulate_paid_action, simulate_paid_action_batch) and the two payment wrappers (pay_then_call, insurelink_charge). While descriptions clarify distinctions, an agent may struggle to choose correctly without careful reading.
All tool names use lowercase snake_case with a consistent verb_noun or verb_noun_noun pattern (e.g., get_reputation, subscribe_attestation_freshness). No mixing of conventions or styles.
With 28 tools, the surface is larger than typical. While each tool serves a specific function, many could be consolidated (e.g., simulation tools, payment wrappers) to reduce cognitive load. The count exceeds the 25-tool threshold for 'too many'.
Core operations for reputation, attestation, SLA lifecycle, and payments are present. However, gaps exist: no tool to list individual SLAs or update an SLA beyond renewal/reset, and no dispute or pricing query tool. The set covers the domain but has notable missing operations.