Commerce activity for an agent or address; job count, unique counterparties, repeat client rate, total earnings. Covers Olas Mechs and Virtuals ACP protocols.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Agent ID. Use with chain and registry for linked agent commerce data. | |
| chain | No | Chain slug (required with id) | |
| address | No | Wallet or contract address (0x...). Use this for Virtuals ACP lookups. | |
| registry | No | Registry: erc8004, olas (default erc8004) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosure. It does state the output metrics and protocol scope, which is useful for a read-only stats tool, but it does not mention data freshness, pagination, error behavior, or parameter precedence. This is adequate but not rich.
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, compact sentence that front-loads the purpose and lists key metrics and protocols without any filler. Every phrase earns its place.
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?
The description lists return metrics, partially compensating for the lack of an output schema, but it leaves ambiguity about parameter requirements (schema marks all as optional) and how id/address/chain combine. These gaps prevent a fuller understanding of successful invocation.
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 describes all four parameters with 100% coverage, so the description adds little beyond what the schema provides. It references agent vs. address but does not clarify required combinations or mutual exclusivity, so the baseline of 3 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 clearly identifies the tool as providing commerce activity data (job count, unique counterparties, repeat client rate, total earnings) for an agent or address, and specifies supported protocols. It is specific and distinct from siblings like network_stats, but it does not explicitly differentiate itself from all sibling tools.
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 gives clear context: it applies to an agent or address and covers Olas Mechs and Virtuals ACP protocols, implying when to use it. However, it does not provide explicit exclusions or mention alternative tools for different use cases.
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 have distinct purposes, but reviewer_analysis and reviewer_wallet overlap in analyzing reviewer behavior, and trust_check and risk_terms both provide trust assessments with different framing. Descriptions help clarify the differences, yet an agent could easily misselect between these pairs.
All tool names use snake_case, but the pattern is inconsistent: some are verb_noun (compare_agents, trust_check) while others are noun-based (address_age, entity, mcp_attestation). This mixed convention is readable but not predictable.
10 tools is well-scoped for an AI agent trust intelligence server, covering both individual lookups and network-wide statistics without unnecessary redundancy. Each tool addresses a specific analytical need.
The server covers the core trust intelligence surface: trust scores, risk assessment, wallet profiling, reviewer analysis, commerce stats, and network stats. Minor gaps exist, such as no tool to fetch a single agent's full profile independent of an operator, but overall the domain is well covered.