Side-by-side trust comparison of 2-10 agents, ranked by score with reviewer quality summary for each.
| Name | Required | Description | Default |
|---|---|---|---|
| agents | Yes | Comma-separated chain:id pairs. Example: base:1380,base:16907 | |
| threshold | No | Pass/fail threshold (default 50) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It reveals that the tool ranks agents by score and provides a reviewer quality summary, which is useful. However, it does not explicitly state that this is a read-only operation, how the threshold parameter affects behavior, or any error/edge-case traits such as handling invalid chain:id pairs.
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 purpose (side-by-side comparison) and then supplies key specifics (2-10 agents, ranking, summary). No wasted words, and all information is relevant.
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 moderate complexity and lack of output schema, the description provides a good high-level summary but omits details like what 'score' means, how the threshold parameter is applied, and the exact structure of the output. It is adequate as a minimal viable description but not fully complete for all agent use cases.
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?
Schema description coverage is 100% for both parameters, so the baseline is 3. The description itself adds no additional meaning about the 'agents' or 'threshold' parameters beyond what the schema already lists. It doesn't explain how the threshold value influences the comparison or the output ranking.
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 uses a specific verb ('compare') and resource ('agents'), and clearly scopes the behavior: side-by-side comparison of 2-10 agents, ranked by score with reviewer quality summaries. This distinguishes it from sibling tools like trust_check (likely single-agent) and reviewer_analysis (likely focuses on one reviewer).
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 does not provide any guidance on when to use this tool versus alternatives. There is no mention of exclusions, prerequisites, or comparative context with sibling tools. It simply states what the tool does, leaving the agent to infer applicability.
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.