Cachly — AI Cognitive Brain

madc_deliberate

Resolve conflicting lessons by deliberating with six specialist agents. Unanimous votes supersede the loser; split votes trigger causal tracing for safe action.

Instructions

Multi-Agent Deliberation Chamber (MADC — Layer 3): When conflicting lessons exist for a topic, run deliberation between 6 specialist expert agents (InfraAgent, AuthAgent, DeployAgent, DatabaseAgent, DebugAgent, APIAgent). Each agent votes based on its domain CKG coverage. Unanimous vote → loser superseded. Split vote → contested flag, causal_trace required before acting. Resolution stored as permanent CKG node. Called automatically when learn_from_attempts detects a contradiction.

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": {
      "instance_id": "<instance_id>",
      "topic": "<topic>"
    },
    "name": "madc_deliberate"
  }
}

Input Schema

Name	Required	Description
`topic`	Yes	Topic to deliberate, e.g. "fix:jwks-rotation"
`context`	No	Optional context for the deliberation
`instance_id`	Yes	Brain instance ID

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.9.9May 6, 2026

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It explains the voting process, the effects of unanimous and split decisions, and the permanent storage of resolutions. However, it does not mention idempotency, rate limits, or authentication needs, which would strengthen transparency.

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 description is a single paragraph that includes both high-level purpose and granular details about voting rules. It is moderately concise but could be split into clearer sections (use case, behavior, output) without losing information.

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?

The description explains the deliberation process well, but it omits what the tool returns (since no output schema is present). It implies manual invocation is possible but focuses on automatic use. For a complex tool, this leaves some ambiguity about expected responses.

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 coverage is 100%, so the schema already documents each parameter. The description does not add semantic details beyond the schema, such as acceptable formats for the topic or how context influences deliberation. Baseline 3 is appropriate.

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 clearly states the tool's purpose: resolving conflicting lessons through multi-agent deliberation. It names the six specialist agents and specifies outcomes (unanimous vs. split vote), and distinguishes itself from siblings by referencing automatic invocation from learn_from_attempts.

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

Usage Guidelines4/5

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

The description conveys when to use the tool—when conflicting lessons exist—and notes that it is called automatically by learn_from_attempts. It mentions causal_trace as a follow-up for split votes, providing context, though it does not explicitly list alternatives or when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server