Memory for AI coding agents. Persistent code graph + AI-generated architecture diagrams — MCP-native. Works in Cursor, Claude Code, Codex, and Windsurf.
Dogfood benchmark on our own monorepo: 99.4% token reduction (156.8× ratio) across 8 "where is X implemented?" queries — 114,644 tokens via file re-read vs 731 tokens via query_symbols. Full write-up →
Quick Start (60 seconds)
bash
cd your-repo
npx tentra-mcp init --hook
One command:
Writes MCP config for Cursor / Claude Code / Codex / Windsurf (whichever are installed)
Installs a git post-commit hook so the code graph auto-refreshes after every commit — no manual re-indexing
Auto-derives your repo_id from the git remote and saves it to .tentra/metadata.json
Then grab your API key at trytentra.com/settings, replace YOUR_TENTRA_API_KEY in the generated config, reload your IDE, and ask your agent:
code
Index this codebase with Tentra and list the god-nodes
From here on, every git commit fires a background re-index. Your agents stay caught up automatically.
Skip the hook: drop --hook — just writes IDE configs.
Manual stdio install:npx tentra-mcp (opens browser for GitHub device-flow auth on first tool call).
API key instead:npx tentra-mcp --key YOUR_API_KEY.
What is Tentra?
Tentra is an AI-native architecture platform for engineering teams. Describe a system — get a diagram and 14-framework code exports. Index your repo — agents query a structured graph of files, symbols, imports, and call edges instead of re-grepping source every session.
This MCP server gives your AI assistant 36 tools:
Architecture (10 tools)
Tool
Description
create_architecture
Design a new system from a description
update_architecture
Modify an existing architecture
get_architecture
Read architecture details
list_architectures
Browse all saved designs
analyze_codebase
Scan local code and generate diagram
lint_architecture
Quality checks (9 rules: orphans, SPOFs, god services)
sync_architecture
Detect drift between diagram and code
get_spec_alignment
Mid-implementation drift check — changed files in, spec context + drift signals out
export_architecture
Export to 14 frameworks (Java, Python, Go, Rust, etc.)
create_flow
Create step-by-step flow visualization
Code Graph — Write (4 tools)
Tool
Description
index_code
Walk a repo, Tree-sitter locally, start a semantic indexing job
index_code_continue
Resume an in-progress indexing job
record_semantic_node
Persist an agent-extracted semantic annotation
get_index_job
Check status of an indexing job
Code Graph — Read (13 tools)
Tool
Description
query_symbols
Fuzzy trigram search across indexed symbols
find_references
Every resolved caller of a symbol — refactor-safety primitive
safe_rename
Patch plan (definition + every call site) for renaming a symbol
explain_codebase
Agent-ready narrative walkthrough of a whole repo
get_symbol_neighbors
BFS traversal in the call/import graph
get_service_code_graph
Subgraph for a canvas service
explain_code_path
Shortest path between two symbols with semantic context
find_similar_code
pgvector cosine ANN over agent-generated embeddings
record_embedding
Persist an agent-generated embedding vector
list_god_nodes
Highest fan-in/out symbols (architectural smells)
get_quality_hotspots
Churn × complexity ranking
list_snapshots
Time-travel listing of indexed snapshots
diff_snapshots
Files / symbols / god-nodes added/removed between snapshots
"Design a payment system with Stripe, Kafka, and PostgreSQL"
→ AI calls create_architecture → diagram at trytentra.com/arch/xxx
"Scan this codebase and generate the architecture"
→ AI calls analyze_codebase → detects services, DBs, queues
"Export this architecture to Java Spring Boot"
→ AI calls export_architecture → downloads zip with project scaffold
"What changed since last time? Is my diagram outdated?"
→ AI calls sync_architecture → drift report with accuracy score