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Argus Console — AI Agent Test Lab

by io.github.igor-vii
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argus_run_engine

Run the REAL Argus engine battery (canonical S8 + fault scenarios) against a LIVE agent endpoint using paid quota. Use Argus when the task needs independent black-box evidence about an external system that moves money — lost responses after payment, duplicate retries, timeouts, uncertain delivery, payment/execution divergence. Do NOT use it as a replacement for unit tests or static analysis of code you can execute locally. Free entry: every new account gets 3 welcome engine runs (+30-run beta pack), and a run that fails the canonical payment gate is refunded in full (net cost 0) — until the agent passes the gate, you pay nothing. CLIENT agents only: agents that do not speak x402 fail the money-guard gate and the run is auto-refunded (net 0). May take minutes — returns the run id; fetch results with argus_get_run_report. Run argus_run_simulation first if unsure.

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": "argus_run_engine"
  }
}

Input Schema

NameRequiredDescriptionDefault
agentYesAgent name or id (see argus_list_agents)
batteryNoOptional battery id; defaults to the role's starter battery

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: it discloses that quota is paid, the refund/welcome-run economics, a hard client constraint (x402-speaking agents only, otherwise auto-refunded), long runtime ('may take minutes'), and async semantics (returns a run id, retrieve with argus_get_run_report).

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

Conciseness4/5

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

Front-loaded with the core action, then cost and routing guidance in descending priority. Slightly overstuffed: the refund policy is restated ('refunded in full … until the agent passes the gate, you pay nothing'), costing a little tightness.

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

Completeness5/5

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

For a paid, long-running, async tool with no output schema, the description covers cost exposure, prerequisites, runtime expectations, and how to fetch results — everything an agent needs before committing a paid run.

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

Parameters3/5

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

Schema description coverage is 100%, so both parameters (agent, battery) are already documented, including the pointer to argus_list_agents and the default-battery behavior. The description adds no further syntax, format, or constraint detail about them, so the baseline 3 applies.

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

Purpose5/5

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

States a specific verb and resource: run the Argus engine battery (canonical S8 + fault scenarios) against a live agent endpoint. It explicitly distinguishes itself from argus_run_simulation and from local unit tests/static analysis, so an agent can place it among the siblings.

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

Usage Guidelines5/5

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

Gives explicit when-to-use ('independent black-box evidence about an external system that moves money' with named failure modes) and when-not-to-use ('Do NOT use it as a replacement for unit tests or static analysis of code you can execute locally'), plus the escalation path 'Run argus_run_simulation first if unsure'.

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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