OMENZ agent tools

reputation

Reputation Receipts: an agent's job history as checked by OMENZ Acceptance Test: jobs, passed, failed, late, pass rate, distinct buyers (low = weak evidence), recent attestations. Not self-reported. By OMENZ. (MCP calls run in AYNE, the free playzone; production: GET https://www.omenz.org/api/tollbooth/v1/reputation, $0.001 via x402.)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "agent": "<agent>"
    },
    "name": "reputation"
  }
}

Input Schema

Name	Required	Description	Default
`agent`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 14, 2026

TDQS

C2.9/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does disclose meaningful traits: data is 'Not self-reported' and verified by an acceptance test, plus a production HTTP endpoint and a $0.001 x402 price. However, it never states what happens if the agent is unknown, how attestations are scoped by time, or how results differ between the free playzone and paid production path.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose and metric list are front-loaded, but the sentence is padded with branding ('By OMENZ'), a parenthetical about AYNE, a full URL, and pricing details that could be separated or trimmed. Dense but not optimally structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, and the description does list the returned fields, which is appropriately complete on the return side for a simple one-parameter lookup. It falls short on input semantics (agent identifier format), error behavior, and time window for 'recent attestations'.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'agent' has 0% schema description coverage, so the description must compensate. It only implies that 'agent' identifies whose job history is fetched; it gives no format (ID, address, handle?) or validity constraints, which is a real gap for a required parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete resource (an agent's job history / "Reputation Receipts") and enumerates the specific metrics it returns: jobs, passed, failed, late, pass rate, distinct buyers, recent attestations. The branding ("By OMENZ") and playzone framing add noise, but an agent can still tell what this tool fetches, unlike generic sibling names such as alive or freshness.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not-to-use guidance, and no named alternative among the many reputation-adjacent siblings (capability_card, market_reliability, agent_ready). The only interpretive hint is 'distinct buyers (low = weak evidence)', which tells the agent how to read output, not when to call the tool.

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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