Agent Quick Start Guide

Version: v0.10.0 Protocol: JSON-RPC 2.0 over HTTP (/cstp) or MCP (/mcp)

What This Is

A decision intelligence server. You query it before making decisions, and it helps you:

• Find what approaches worked (or failed) for similar problems
• Check if guardrails allow your planned action
• Record decisions with structured reasoning for future reference
• Build calibration data so your confidence estimates improve over time

Quick Start

1. Connect

JSON-RPC:

POST http://<server>:9991/cstp
Authorization: Bearer <your-token>
Content-Type: application/json

MCP: Connect to http://<server>:9991/mcp with any MCP-compliant client.

2. Before Deciding: Query + Check

// Step 1: What solved problems like this?
{"jsonrpc":"2.0","method":"cstp.queryDecisions","params":{
  "query":"handling transient API failures",
  "bridgeSide":"function",
  "limit":5
},"id":1}

// Step 2: Am I allowed to do this?
{"jsonrpc":"2.0","method":"cstp.checkGuardrails","params":{
  "action":{
    "description":"Add retry logic to payment API",
    "category":"architecture",
    "stakes":"high",
    "confidence":0.85
  }
},"id":2}

3. After Deciding: Record (via pre_action — preferred)

{"jsonrpc":"2.0","method":"cstp.preAction","params":{
  "action":{
    "description":"Add exponential backoff with jitter to payment API",
    "category":"architecture",
    "stakes":"high",
    "confidence":0.85
  },
  "auto_record":true,
  "reasons":[
    {"type":"analysis","text":"Backoff handles transient failures; jitter prevents thundering herd"},
    {"type":"empirical","text":"Similar pattern succeeded in order-service (decision abc123)"}
  ],
  "tags":["resilience","retry","payment"],
  "pattern":"Exponential backoff with jitter for transient failures"
},"id":3}

This queries + checks guardrails + records in one call, returning a decisionId.

4. During Work: Capture Reasoning

{"jsonrpc":"2.0","method":"cstp.recordThought","params":{
  "text":"Chose 3 retries with base 500ms - matches P99 latency recovery window",
  "decision_id":"<decisionId from step 3>"
},"id":4}

5. After Work: Finalize

{"jsonrpc":"2.0","method":"cstp.updateDecision","params":{
  "id":"<decisionId>",
  "decision":"Added exponential backoff (3 retries, 500ms base, jitter) to payment API",
  "context":"Deployed to staging, 503 rate dropped from 2.1% to 0.03%"
},"id":5}

6. What You Get Back

The server automatically attaches:

• Deliberation trace: Your queries and reasoning steps linked to this decision
• Bridge-definition: Structure (what it looks like) + function (what it solves) extracted from your text
• Related decisions: Past decisions found during pre_action, linked with distance scores
• Graph edges: Auto-linked to related decisions

{
  "result":{
    "id":"def456",
    "indexed":true,
    "deliberation_auto":true,
    "deliberation_inputs_count":2,
    "bridge_auto":true,
    "related_count":5
  }
}

Adding to Your Agent's System Prompt

Paste this into your agent's AGENTS.md or system prompt:

## Decision Protocol

Before making significant decisions (architecture, tooling, process):

1. **Decide + Record**: Use pre_action with auto_record to query, check guardrails, and record in one call
   POST /cstp {"method":"cstp.preAction","params":{"action":{"description":"what you want to do","category":"architecture","stakes":"medium","confidence":0.85},"auto_record":true,"reasons":[{"type":"analysis","text":"why"}],"tags":["keyword"],"pattern":"abstract principle"}}

2. **Think**: Capture reasoning during work (use decisionId from step 1)
   POST /cstp {"method":"cstp.recordThought","params":{"text":"reasoning...","decision_id":"<decisionId>"}}

3. **Finalize**: Update the decision with what actually happened
   POST /cstp {"method":"cstp.updateDecision","params":{"id":"<decisionId>","decision":"what you actually did","context":"outcome details"}}

4. **Review** (later): Record success/failure for calibration
   POST /cstp {"method":"cstp.reviewDecision","params":{"id":"<decisionId>","outcome":"success","result":"what happened"}}

Use at least 2 different reason types for robustness.
For multi-agent setups, pass agent_id to isolate deliberation streams.

Tips

• Use 2+ reason types for robustness: analysis, empirical, pattern, authority, constraint, analogy, intuition, elimination
• bridgeSide search: Use "function" to find what solved similar problems, "structure" to find where a pattern was used before
• Confidence honestly: Rate your actual uncertainty, not optimism. The calibration data is more valuable than a clean record.
• Review outcomes: Call cstp.reviewDecision when you know the result. This builds calibration data.

What to Log

• Architecture and design choices
• Tool or library selections
• Process changes
• Bug fix approaches
• Any choice that could be wrong and worth reviewing

What NOT to Log

• Routine operations (backups, deploys)
• Trivial formatting choices
• Decisions already made by someone else

Available Methods

Method	Purpose
cstp.queryDecisions	Search past decisions (semantic, keyword, or hybrid)
cstp.checkGuardrails	Validate an action against policy rules
cstp.recordDecision	Log a new decision with reasoning
cstp.reviewDecision	Record the outcome of a past decision
cstp.getDecision	Get full decision details by ID
cstp.getCalibration	Calibration stats (Brier score, accuracy)
cstp.getReasonStats	Which reason types predict success
cstp.listGuardrails	List active guardrail rules
cstp.checkDrift	Detect calibration drift over time
cstp.attributeOutcomes	Bulk outcome attribution by project

Categories

architecture, process, integration, tooling, security

Stakes

low, medium, high, critical