Inspect
See tables, columns, keys, and relationships as a clean structural map.
schemap inspect
The dangerous kind of wrong
AI can confidently generate a query that matches familiar naming patterns—but misses the relationships that are true in your database.
SELECT orders.id, users.email
FROM orders
JOIN users
ON orders.customer_id = users.id;
The query looks reasonable. Your schema has no
orders.customer_id.
orders.account_id
↓
accounts.id
↓
users.account_id
Schemap gives the agent the relationship map it was missing.
The missing context
Raw dumps describe structure, but bury the context an agent needs to make reliable database decisions.
Foreign keys and join paths disappear inside a wall of DDL.
acct_id may mean something completely different in your domain.
Noise consumes the context window before the real problem begins.
A context layer for your database
Claude Code · Cursor · Codex · Copilot · Custom agents
One command
Schemap runs locally, extracts the relationships that matter, and writes context files your existing workflow can use.
Try it in your project ↗$ schemap context
→ Inspecting schema...
→ Mapping foreign-key relationships...
→ Compiling AI context...
✓ Context written to database_context.md
$ schemap benchmark
Measure raw vs compiled tokens on your schema.
Measure, don’t guess
Token savings vary by schema. The built-in benchmark reports your raw estimate, compiled context size, relationship coverage, readiness score, and compile latency.
↓Token footprintMeasured per database
◎AI readinessActionable schema score
↗Relationship clarityExplicit join paths
The workflow
See tables, columns, keys, and relationships as a clean structural map.
schemap inspect
Find missing keys, disconnected entities, and ambiguous names that hurt AI accuracy.
schemap score
Compile compact Markdown, JSON, YAML, XML, MCP, or AI-ready context files.
schemap context
Track structural schema changes between runs and keep agent context current.
schemap diff
Generate CLAUDE.md and AGENTS.md files with the database rules your agents need.
schemap agents
Built for developer workflows
Extraction and compilation run locally by default. Schemap produces files you can inspect, commit, review, and inject into the tools you already use.
Supported databases
PostgreSQL · SQLite · MySQL · Turso/libSQL · Oracle
Optional enrichment--enrich can use the configured OpenAI API
to improve business descriptions.
License checks
Licensed CI/CD usage verifies a license online; local free
usage does not require an external AI service.
Start locally in 60 seconds
Install the CLI with pipx (or pip / uv), point it at your schema, and compile instant AI-ready context.
# 1. Install developer CLI globally
pipx install schemap-tool
# 2. Verify installation
schemap --version
# 3. Initialize & compile context
schemap init
schemap context
# Activate Pro key anytime for unlimited tables & CI/CD
schemap activate YOUR_LICENSE_KEY
2026 Founder Program
The full local CLI is available for free. Pro unlocks larger schemas, CI/CD automation, and production workflow support.
For local projects and evaluation.
Flexible monthly billing.
One payment for lifetime Pro access.
Developer FAQ
Not by default. Extraction and compilation run locally. Optional --enrich sends
compressed schema metadata to the configured OpenAI API. Licensed CI/CD usage also performs
an online license check.
No. Schemap creates context for agents and SQL workflows. Your ORM, migrations, and application remain the source of truth for runtime behavior.
Yes. Generate Markdown context, CLAUDE.md, AGENTS.md, JSON, MCP, or framework exports for the workflow your agent already reads.
PostgreSQL, SQLite, MySQL, Turso/libSQL, and Oracle are supported by the current extractors.
Give your database a context layer
Make the relationships explicit before your agent starts writing SQL.