Schemap vs. tbls
Why human database documentation tools waste 80% of LLM context windows, and how Schemap's Database Intelligence Layer equips AI agents with deterministic context, safety rules, and sub-3ms performance.
Direct Comparison Matrix
Direct Answer: tbls is an excellent Go-based tool built to generate human-readable HTML/Markdown documentation websites. Schemap is built specifically for AI agents (Claude Code, Cursor, Codex, Copilot), delivering 80% token compression, anti-hallucination safety rules (`CLAUDE.md`, `AGENTS.md`), role-scoped context filtering (`--scope`), and sub-3ms local CLI speed.
| Capability / Feature | `tbls` (Human Docs) | Schemap v3.1.0 (AI Intelligence) |
|---|---|---|
| Target Consumer | Human Web Browsers & Wiki Readers | AI Coding Agents (Claude Code, Cursor, Copilot) |
| Context Token Efficiency | ❌ Low (HTML/Markdown bloat) | ✅ Up to 80% Compressed (`schemap benchmark`) |
| AI Anti-Hallucination Guardrails | ❌ None | ✅ Automatic `[SAFETY]` rule injection (`CLAUDE.md`) |
| Role-Scoped Context Profiles | ❌ No | ✅ `--scope analytics`, `--scope backend`, `--scope core` |
| Candidate Foreign Key Inference | ❌ No | ✅ Confidence scoring & interactive fix (`schemap fix`) |
| AI Readiness Health Score | ❌ No | ✅ Diagnostic health suite 0-100 (`schemap doctor`) |
| Shortest Join Path Solver | ❌ No | ✅ Built-in BFS Join Graph Solver (`schemap join`) |
| Compilation Latency | ~100ms - 250ms | Sub-3ms deterministic compilation |
When to Use tbls vs. When to Use Schemap
When to Pick tbls
- Your primary target is human software engineers browsing a GitHub Wiki or HTML documentation website.
- You need PlantUML or SVG diagram output for static architecture reviews.
- You do not prompt AI coding agents directly with database context files.
When to Pick Schemap
- Your team uses Claude Code, Cursor, GitHub Copilot, or Codex for daily feature development.
- You want to eliminate AI JOIN hallucinations and prevent sensitive credentials (`password_hash`) from leaking into LLM prompts.
- You need to cut LLM token costs by 80% on large production schemas (50 to 500+ tables).
- You want automated context synchronization in CI/CD and team pre-commit hooks (`schemap hook install`).
Key Differences Explained
1. Token Footprint: Human Formatting vs. AI Context Engineering
tbls outputs elaborate Markdown tables complete with HTML badges, full column attribute tables, and verbose text formatting optimized for human eyes. When fed to an LLM, this extra formatting consumes thousands of unnecessary input tokens. Schemap extracts pure relational semantics, achieving 80% token compression so your AI agent retains maximum reasoning memory.
2. Native Agent Rule Integration (`CLAUDE.md`, `AGENTS.md`)
Instead of leaving AI agents to guess database conventions from raw docs, Schemap compiles purpose-built rule files (CLAUDE.md, AGENTS.md, Cursor .mdc) complete with explicit negative guardrails protecting financial and audit tables.
3. Diagnostic AI Readiness Scoring (`schemap doctor`)
Schemap analyzes your database schema for AI clarity (0-100 score), highlighting missing foreign keys, unmapped column abbreviations (`tx_id`, `cust_num`), and un-commented central tables before your AI agent attempts to write SQL.