Full operator footprint for any wallet address — all agents owned, trust scores, sybil signals, wallet intelligence, and MCP servers associated with that operator.
| Name | Required | Description | Default |
|---|---|---|---|
| wallet | Yes | 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 disclosure. It details what data is returned (agents, trust scores, sybil signals, etc.), which adds transparency. However, it does not discuss return format, potential latency, or any operational side effects, limiting full behavioral transparency.
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, well-structured sentence that front-loads the core action ('Full operator footprint') and then enumerates the included data points. Every word adds value, with no redundancy or filler.
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 single-parameter lookup tool with no output schema, the description adequately communicates the scope of results by listing five data categories. It is complete enough for basic understanding, though it could briefly mention how results are presented (e.g., aggregated vs raw) to be fully self-sufficient.
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 schema already fully describes the sole parameter 'wallet' as 'Wallet address (0x...)', giving 100% coverage. The description adds no additional parameter context beyond the schema, 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 states the tool's purpose: 'Full operator footprint for any wallet address' and lists specific data categories (agents owned, trust scores, sybil signals, wallet intelligence, MCP servers). This distinguishes it from sibling tools like address_age or trust_check, which focus on narrower metrics.
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 this is the go-to tool for a comprehensive operator overview ('Full operator footprint'), but it does not explicitly state when to use it versus alternatives, nor does it mention any exclusions or prerequisites beyond providing a wallet address.
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.