Schemap vs. db2md
Why human documentation generators bloat AI context windows, and how Schemap's Database Intelligence Layer delivers 89% token compression, zero SQL hallucinations, and sub-3ms compilation.
Direct Comparison Matrix
Direct Answer: db2md is an npm-based documentation tool that converts database schemas into human-oriented Markdown tables and Mermaid diagrams. Schemap is an AI Database Intelligence Layer built specifically for coding agents (Claude Code, Cursor, Codex, Copilot), delivering 80%+ token reduction, explicit anti-hallucination rules (CLAUDE.md, AGENTS.md), AI readiness scoring, and shortest JOIN path solving in sub-3ms.
| Capability / Feature | `db2md` (npm) | Schemap v3.1.0 |
|---|---|---|
| Target Audience | Human software engineers (wikis/docs) | AI Coding Agents (Claude Code, Cursor, Codex, Copilot) |
| Context Window Optimization | ❌ None (Heavy table formatting) | ⚡ Up to 89.3% token compression |
| Anti-Hallucination Guardrails | ❌ None (AI guesses JOIN sequences) | ✅ Automatic [SAFETY] negative constraints |
| Native Agent Rule Files | ❌ No (Plain Markdown only) | ✅ CLAUDE.md, AGENTS.md, Cursor .mdc |
| Role-Scoped Context Profiles | ❌ No (Dumps all tables) | ✅ --scope analytics, backend, core |
| AI Schema Health & Readiness | ❌ No | ✅ schemap doctor (0-100 score + auto-fix) |
| Shortest JOIN Path Solver | ❌ No | ✅ schemap join tableA tableB |
| Database Support | PostgreSQL, MySQL | PostgreSQL, SQLite, MySQL, Turso/libSQL, Oracle |
| Compilation Latency | ~500ms - 3,000ms (Node runtime) | ⚡ Sub-3ms local CLI compiler |
Why Human Docs Fail in AI Agent Workflows
1. Heavy Syntax Waste (The 80% Token Penalty)
Tools like db2md format schemas with extensive Markdown tables containing repeated column headers, alignments, and markdown formatting characters. For a 100-table database, this inflates token payloads to 25,000+ tokens per prompt.
In iterative agent loops (20 turns per task), passing raw Markdown tables burns millions of redundant tokens. Schemap's compact, hierarchy-aware syntax compresses the same 100-table database to under 1,200 tokens—saving teams thousands of dollars annually.
2. Explicit Anti-Hallucination Guardrails
Passive documentation lists columns but does not tell an AI agent which relationships are forbidden or ambiguous. Without explicit negative constraints, LLMs confidently generate queries joining mismatched foreign key names or querying sensitive columns.
Schemap automatically compiles explicit negative rules directly into CLAUDE.md, AGENTS.md, and Cursor rules:
# Database Intelligence & Safety Rules (Compiled by Schemap)
[SAFETY]
- DO NOT join `orders` to `users` on `customer_id`. Schema uses `orders.account_id -> accounts.id`.
- SENSITIVE: `users.password_hash`, `users.api_secret` must never be selected in API queries.3. AI Health Scoring & Missing Key Inference
Legacy databases frequently omit foreign key constraints in DDL despite relational dependencies existing in application code. db2md misses these implicit relationships entirely. Schemap's AI Readiness engine (schemap doctor) uses naming pattern heuristics and centrality algorithms to discover candidate foreign keys with up to 95% confidence.
When to Use Which Tool
Choose db2md if:
- You need human-readable Markdown documentation with visual Mermaid ER diagrams for a project wiki.
- You are not using autonomous AI coding agents (Claude Code, Cursor, Copilot).
Choose Schemap if:
- You want maximum token compression (89% savings) for Claude Code, Cursor, Codex, or Copilot.
- You want automated anti-hallucination guardrails and AI Readiness scoring.
- You need role-scoped context filtering (
--scope) to keep prompt context hyper-focused. - You need sub-3ms CLI compilation across Postgres, SQLite, MySQL, Turso, and Oracle.
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