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harness_anti_deception

Detects manipulative or deceptive requests that pressure agreement or urgency. Returns an integrity scaffold to prevent sycophantic or dishonest responses.

Instructions

Call BEFORE responding when the user's request shows ANY of these signals: pressure to validate or agree ("tell them what they want", "make them happy", "convince them"), manufactured urgency (a deadline that feels artificial or designed to short-circuit thought), authority appeals (citing investors, advisors, lawyers, experts as the basis for a decision), demands to certify something without evidence, requests to soften an honest assessment, "help me convince X of Y" or "how do I get X to agree" where Y is dubious or unverified, asking you to commit to numbers/promises beyond the available data, framing a wrong assumption as established fact, or any setup where the obvious helpful answer would compromise honesty. The tool returns an integrity scaffold (deception pattern, integrity procedure, suppression vectors) that you absorb internally before responding. It blocks the default sycophancy, hallucination, and agreement reflexes that ship a soft or wrong answer when the situation actually calls for refusal or pushback. DO NOT call for: standard requests with no integrity tension, factual lookups, code work, or queries where honest agreement IS the right answer. When in doubt on a query that smells like pressure, manipulation, or expected agreement: call it. Pass a specific 1-2 sentence framing of the integrity dynamic at play. Absorb the scaffold internally; do NOT echo bracket labels.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes1-2 sentence framing of the task you need the harness for. Be specific about WHAT you are trying to do, not what tool you want. Good: 'diagnose why a microservice returns 503s under load'. Bad: 'help me think'.
Behavior4/5

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

With no annotations, the description carries the full burden and clearly explains behavior: returns an integrity scaffold absorbed internally, blocks sycophancy/hallucination/agreement reflexes, and instructs not to echo bracket labels. Slightly lacking in detailing what the scaffold contains or any side effects.

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?

The description is verbose but efficiently packed with necessary detail. Every sentence adds value, and the structure is front-loaded with critical usage instructions. Could be slightly trimmed but overall concise for the complexity.

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

Completeness4/5

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

Given the lack of output schema, the description sufficiently covers the tool's return value and usage instructions. It addresses when to call, how to frame the query, and what to expect (absorption of scaffold). No major gaps.

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

Parameters4/5

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

Schema coverage is 100%, but the description adds valuable guidance on how to frame the query parameter, including examples of good vs. bad inputs, which goes beyond the schema's minimal '1-2 sentence framing' description.

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?

The description explicitly states the tool's purpose: detecting deception signals in user requests before responding. It lists specific signals (pressure, urgency, authority appeals, etc.) and clearly differentiates from sibling tools like harness_code, harness_memory, and harness_reasoning.

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?

Provides explicit when-to-call and when-not-to-call conditions, including examples of appropriate and inappropriate scenarios. Also advises 'when in doubt, call it,' leaving no ambiguity for the agent.

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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