tentra-mcp
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
npx tentra-mcp
On first use, your browser opens for one-click GitHub sign-in. That's it — no API key needed.
Advanced: If you prefer to use an API key directly, run
npx tentra-mcp --key YOUR_API_KEY. Get your key at trytentra.com/settings.
What is Tentra?
Tentra is the persistent memory layer for AI coding agents. 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 32 tools:
Architecture (9 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 |
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 (10 tools)
| Tool | Description |
|---|---|
query_symbols |
Fuzzy trigram search across indexed symbols |
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 |
Enrichment — Contracts, Decisions, Ownership, Domains (9 tools)
| Tool | Description |
|---|---|
set_service_mapping |
Link an indexed file or symbol to a canvas service |
set_domain_membership |
Assign a service or file to a domain (bounded context) |
record_contract |
Store a parsed API contract payload (OpenAPI, GraphQL, Protobuf) |
bind_contract |
Link a contract to the symbol that implements it |
record_decision |
Create an Architecture Decision Record, optionally linking code |
link_decision |
Append a link from an ADR to another symbol, file, or service |
get_ownership |
Resolve the owner (team or person) for a file or service |
get_decisions_for |
List ADRs linked to a given entity |
get_contracts |
List contracts, optionally filtered by kind or service |
Setup
Option 1: SSE (zero install)
Add to your IDE's MCP config — no local install needed:
Cursor (Settings > Features > MCP > Add Server):
{
"tentra": {
"type": "sse",
"url": "https://trytentra.com/api/mcp?key=YOUR_API_KEY"
}
}
Claude Code (.mcp.json in project root):
{
"mcpServers": {
"tentra": {
"type": "sse",
"url": "https://trytentra.com/api/mcp?key=YOUR_API_KEY"
}
}
}
Option 2: Local install (needed for codebase scanning)
npx tentra-mcp
Authenticates automatically via GitHub on first use. Credentials are saved to ~/.tentra/credentials.
Cursor config for local server:
{
"tentra": {
"command": "npx",
"args": ["tentra-mcp"]
}
}
Claude Code (.mcp.json):
{
"mcpServers": {
"tentra": {
"command": "npx",
"args": ["tentra-mcp"]
}
}
}
Usage Examples
Once connected, just talk to your AI:
"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
Export Formats
Java (Spring Boot), Node.js (Fastify), Python (FastAPI), Go (chi), Rust (Axum), .NET (ASP.NET), Kotlin (Ktor), PHP (Laravel), Ruby (Rails), Elixir (Phoenix), Docker Compose, Mermaid, ADR, Terraform
Links
- Website: trytentra.com
- Documentation: trytentra.com/docs
- Setup Guide: trytentra.com/docs/setup
- Gallery: trytentra.com/gallery
Development
This repo contains the open-source MCP server. The Tentra API and web app are a separate hosted service at trytentra.com.
npm install --legacy-peer-deps
npm run build # tsc --noEmit + esbuild bundle → dist/index.js
npm start # run the bundled server
npm test # vitest
The published npm package (tentra-mcp) ships only the bundled dist/ — source is here for auditability and community contributions.
License
MIT
