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gpkg-mcp

by truongtnn404
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Python
Hybrid

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.call dependencies (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_version so improved extractors always trigger a re-scan

Related MCP server: code-graph-mcp

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).

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Maintenance

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