gpkg-mcp
A knowledge graph MCP server for Moleculer and Go microservice projects that scans codebases, extracts service metadata, seeds Neo4j, and exposes tools for LLM clients to query and understand service architecture.
README
gpkg-mcp
Standalone knowledge graph MCP server for Moleculer/Go microservice projects. Scans a project directory, extracts service metadata (actions, commands, queries, routes, events, dependencies), seeds Neo4j, and exposes everything via a Python MCP server that any LLM client (Claude Desktop, Cursor, the gp-langchain bot) can call.
What it does
- Scans TypeScript/JavaScript Moleculer services (layered, CQRS, gateway) and Go services
- Extracts per-action metadata: handler type, params validator,
ctx.calldependencies (regex + variable resolution) - Seeds Neo4j knowledge graph and writes per-service markdown docs
- Serves a FastMCP server over Streamable HTTP or stdio
- Caches scan fingerprints with a
schema_versionso improved extractors always trigger a re-scan
Quick start (with sample data)
# One-shot bootstrap: checks prereqs, seeds sample services, starts MCP
./start.sh
The script: checks uv + Neo4j, runs uv sync, seeds sample/ data into Neo4j, then starts the server on http://localhost:8000/mcp.
Manual setup
cp .env.sample .env
# edit .env — set GPKG_NEO4J_URI / GPKG_NEO4J_PASSWORD at minimum
uv sync
# Scan a project
uv run gpkg scan ../my-project
# Query the knowledge graph
uv run gpkg query "payment transfer"
# Start the MCP server
uv run gpkg serve
Commands
| Command | Description |
|---|---|
gpkg scan <dir> |
Scan all services under <dir>, seed Neo4j, write markdown KB |
gpkg scan <dir> --use-llm |
Enable LLM validation for low-confidence architecture detections |
gpkg scan <dir> --force |
Bypass fingerprint cache, rescan everything |
gpkg scan <dir> --concurrency N |
Set parallel scan concurrency (default: 5) |
gpkg query "<text>" |
Full-text query against the seeded KG |
gpkg query "<text>" --service NAME |
Filter results to one service |
gpkg serve |
Start MCP server (streamable-http on port 8000) |
gpkg serve --transport stdio |
Start for Claude Desktop / Cursor local use |
MCP tools
| Tool | Description |
|---|---|
scan_project(project_dir, use_llm, force) |
Scan all services; returns {scanned, seeded, skipped, failed, services} |
scan_service(project_dir, service_path, use_llm) |
Scan a single service; returns EnhancedServiceScanResult |
query_knowledge_graph(query, service_name?) |
Full-text search; returns {services, actions, relationships} |
get_service(service_name) |
Full stored knowledge for one service |
list_services() |
All known services with arch type + confidence |
Resource: kb://{service_name} — the generated markdown doc for a service.
Configuration
Copy .env.sample to .env:
| Variable | Default | Description |
|---|---|---|
GPKG_NEO4J_URI |
bolt://localhost:7687 |
Neo4j bolt URI |
GPKG_NEO4J_USER |
neo4j |
Neo4j username |
GPKG_NEO4J_PASSWORD |
(empty) | Neo4j password |
GPKG_KB_DIR |
./knowledge_base |
Markdown KB output directory |
GPKG_SCAN_CONCURRENCY |
5 |
Parallel scan workers |
GPKG_LLM_ENABLED |
false |
Enable LLM validation pass |
GPKG_LLM_MODEL |
claude-haiku-4-5-20251001 |
Model for architecture validation |
GPKG_LLM_CONFIDENCE_THRESHOLD |
0.7 |
Only run LLM when confidence is below this |
GPKG_API_KEY |
(empty) | Bearer token for MCP server (empty = unauthenticated) |
ANTHROPIC_API_KEY |
(empty) | Required when GPKG_LLM_ENABLED=true |
Docker
# Build
docker build -t gpkg-mcp .
# Run with a local .env and data volume
docker run --env-file .env -p 8000:8000 -v $(pwd)/knowledge_base:/data/knowledge_base gpkg-mcp
# Scan a project (mount it read-only)
docker run --env-file .env \
-v $(pwd)/knowledge_base:/data/knowledge_base \
-v /path/to/my-project:/project:ro \
gpkg-mcp uv run gpkg scan /project
Architecture detection
| Type | Detection signal |
|---|---|
layered-moleculer |
actions: block with inline handlers in start.ts or *.service.ts |
cqrs-moleculer |
CQRSContainer, CommandBus, QueryBus keywords |
gateway-moleculer |
integrate-services/ directory with RestRoute() definitions |
golang-moleculer |
go-moleculer in go.mod + Publisher pattern |
discrete-golang-moleculer |
go-moleculer in go.mod, no Publisher |
external-api-service |
No Moleculer; Express / Gin / Echo / Fiber |
Confidence levels: high → medium → low. LLM validation runs when confidence is below GPKG_LLM_CONFIDENCE_THRESHOLD.
Cache schema version
knowledge/cache.py has a CACHE_SCHEMA_VERSION constant (currently 4). Bump it whenever an extractor changes — the next scan will ignore all cached fingerprints and re-scan everything.
Running tests
uv run pytest
Tests cover: LayeredMoleculerExtractor action/handler/call extraction, cache schema version invalidation, and MCP tool shapes (mocked store, no Neo4j needed).
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。