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reopt Handbook
reopt Handbook
Agentic Documentation

Paradigm Shift

Why Documentation Must ChangeAI-readable Design PrinciplesAI-assisted Writing Process

Practice by Document Type

Project Rule DocumentsFrom Docs to Skills, Plugins, and MCPAgent Documentation SecurityAPI Docs and SpecsREADME and OnboardingArchitecture Decision RecordsRunbooks and OperationsUser Guides and TutorialsKnowledge Bases and Internal Wikis

Operations

Maintenance StrategyTeam Documentation Culture

Appendix

TemplatesVerification ReportUpdates
Handbook›에이전틱 시대의 문서화 혁신›Knowledge Bases and Internal Wikis
한국어English

Knowledge Bases and Internal Wikis

Design KBs for RAG, MCP Resources, freshness, and citation-first answers.

Key takeaways

  • KBs are core data sources for RAG and MCP Resources; structure decides whether agents retrieve the right doc and cite it or pull stale, irrelevant content.
  • Keep docs atomic (one topic, one conclusion) so RAG retrieves targeted chunks instead of a single 100-page guide.
  • Rich frontmatter (id, status, owner, updated, review_after, source_of_truth) and chunking that carries doc ID and heading metadata enable retrieval and demoting of stale docs.
  • Use a citation-first answer format (answer, evidence with doc_id/section/updated, uncertainty, owner to confirm) and validate retrieval with expected/must-not-return eval sets.
  • Resource descriptions help the model, but Resource text stays data; do not promote an external KB document to project instructions.

Knowledge bases are core data sources for RAG and MCP Resources. If the KB is well structured, agents can retrieve the right document and cite it. If not, they retrieve stale or irrelevant content.

Design Goals

GoalMeaning
Retrievaltitle, description, tags, and headings help search
Citationanswers can point to doc IDs and sections
Freshnessstale docs are lowered or excluded
Trust boundaryexternal text is data, not instructions

Atomic Documentation

One topic per document. One conclusion per evidence trail.

MonolithicAgent-optimized
100-page "developer guide"20 focused docs
facts hidden in the middletitle/description identify scope
RAG retrieves too muchRAG retrieves targeted chunks
unclear sourcedoc ID and heading citation

Frontmatter

---
id: kb-db-postgres-connection
title: PostgreSQL Connection Settings
description: DB connection settings for dev, staging, and production
category: infrastructure/database
tags: [postgresql, database, connection, environment]
status: current
owner: infra-team
created: 2026-01-15
updated: 2026-05-24
review_after: 2026-08-24
source_of_truth: /docs/env-variables
related:
  - /docs/db-migration
  - /docs/secrets
---

Chunking

StrategyRecommendation
unitsplit by H2/H3 when possible
metadatacarry doc ID, title, heading, status, updated
tableskeep heading and column meaning
code blockskeep command and explanation together
stale docsdemote or exclude from retrieval

MCP Resource URIs

kb://docs/db/postgres-connection
kb://runbooks/api-latency
kb://policies/refund-policy
schema://warehouse/orders
openapi://public-api/latest

Resource descriptions help the model, but Resource text is still data. Do not treat an external KB document as a source of project instructions.

Citation-first Answer Format

1. Answer
2. Evidence
   - doc_id:
   - section:
   - updated:
3. Uncertainty
4. Owner to confirm

Retrieval Eval

[
  {
    "question": "Where is the production DB connection string configured?",
    "expected_docs": ["kb-db-postgres-connection"],
    "must_not_return": ["kb-db-legacy-connection"]
  }
]

Checklist

ItemCheck
docs are atomic[ ]
id, status, owner, updated, review_after exist[ ]
chunk metadata includes doc ID and heading[ ]
stale docs are demoted or excluded[ ]
answers cite sources[ ]
MCP Resource URI maps to source URL[ ]
external/user text is not promoted to instructions[ ]

References

  • Model Context Protocol: Understanding MCP servers
  • Model Context Protocol Schema Reference
  • OWASP Top 10 for LLM Applications

Related docs

RAG and Retrieval-Augmented Generation

Vercel Enterprise AI Platform · Build retrieval pipelines with indexing, permission filtering, reranking, and evaluation.

Agent Documentation Security

Reduce prompt injection, MCP tool poisoning, Plugin supply chain risk, and excessive agency.

From Docs to Skills, Plugins, and MCP

Split rules, workflows, distribution units, and external context into the right agent surface.

Customer Support Agent Architecture

Vercel Enterprise AI Platform · Design customer-facing support agents with retrieval, escalation, fallback, and audit.

Cmd. /title

Codex Command Master · Configure terminal window or tab title fields.

User Guides and Tutorials

Structure user-facing documentation so agents can answer product questions with grounded context.

Maintenance Strategy

Keep docs, instructions, Skills, Plugins, MCP Resources, and validation loops from drifting.

On this page

Design GoalsAtomic DocumentationFrontmatterChunkingMCP Resource URIsCitation-first Answer FormatRetrieval EvalChecklistReferences