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Systemores AI Agent & MCP Security Auditor

Evaluate Multi-Turn Behavioral Drift

systemores_evaluate_drift
Read-only

Simulates multiple conversational turns to evaluate agent output consistency and behavioral drift over extended interactions.

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": {
      "systemPrompt": "<systemPrompt>"
    },
    "name": "systemores_evaluate_drift"
  }
}

Input Schema

NameRequiredDescriptionDefault
turnsNoThe number of conversational turns to simulate (1–100).
temperatureNoThe LLM temperature parameter for the target agent (0.0–1.5). Higher values increase drift probability.
systemPromptYesThe target agent's system prompt. Maximum 8,000 characters.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=false, and openWorldHint=true, so the safety profile is covered. The description adds the useful fact that the tool runs repeated simulated turns, but it omits the practical consequences of that — repeated LLM invocations against a target agent, cost/latency, and non-determinism that the idempotentHint=false implies.

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?

A single well-formed sentence that front-loads the action and flows into the measured outcome, with no filler. It is efficient, though it is thin enough that brevity edges toward under-specification.

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, so the description should clarify what a run produces (drift report, per-turn scores, flags). The purpose sentence gestures at the evaluated qualities but never states the return shape, prerequisites for the target agent, or the cost of a 100-turn run. With fully documented parameters and a rich annotation set, this is adequate but not complete.

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%, and all three parameters are documented in the schema, including the note that higher temperature increases drift probability. The description mentions no parameters at all, so it adds nothing beyond the schema; baseline 3 applies.

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 pairs a specific verb ("simulates") with a specific mechanism ("multiple conversational turns") and a named outcome ("output consistency and behavioral drift"). It is clear what the tool does, but it never distinguishes itself from plausible siblings such as stress_test_agent or audit_turn_live, so an agent must still infer which one applies.

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

Usage Guidelines3/5

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

Usage is only implied by the phrase "over extended interactions," which hints at long-horizon, multi-turn scenarios. There is no explicit when-to-use statement, no when-not-to-use condition, and no named alternative, leaving routing between this and the drift/stress siblings to inference.

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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