에이전트 강화
게시자의 자체 명령입니다. PlugMCP 엔드포인트가 아닙니다.
Host a copy with our keys and logs.
npx -y [email protected]Claude Code에는 로컬 명령용 설치 링크가 없습니다. 아래 명령을 복사해 터미널에서 실행합니다.
DATABASE_URL=<value> EMBEDDING_PROVIDER=<value> OPENAI_API_KEY=<secret> GEMINI_API_KEY=<secret> VOYAGE_API_KEY=<secret> claude mcp add io.github.iamvirul-wso2-docs-mcp-server -- npx -y wso2-docs-mcp-server@1.3.16터미널에서 실행합니다.
{ "mcpServers": { "io.github.iamvirul-wso2-docs-mcp-server": { "command": "npx", "args": [ "-y", "[email protected]" ], "env": { "DATABASE_URL": "<value>", "EMBEDDING_PROVIDER": "<value>", "OPENAI_API_KEY": "<secret>", "GEMINI_API_KEY": "<secret>", "VOYAGE_API_KEY": "<secret>" } } }}claude_desktop_config.json
원격 URL인가요, 내 컴퓨터에서 실행하는 명령인가요? 두 설치 방식의 차이
PlugMCP에서 실행
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
실행게시자 제공
"This is an unofficial community project. Not affiliated with or endorsed by WSO2."
A production-ready Model Context Protocol (MCP) server that provides AI assistants (Claude Desktop, Claude Code, Cursor, VS Code) with semantic search over WSO2 documentation via Retrieval-Augmented Generation (RAG).
Semantic search over WSO2 docs (APIM, IS, MI, Choreo, Ballerina) via RAG and pgvector.
Under the hood, it uses a blazing-fast dual-ingestion engine:
Choose the setup path that fits your use case:
Install the package globally to get the wso2-docs-mcp-server, wso2-docs-crawl, and wso2-docs-migrate commands available system-wide:
npm install -g wso2-docs-mcp-server
Prefer no global install? You can use
npx wso2-docs-mcp-server,npx wso2-docs-crawl, andnpx wso2-docs-migratein every step below - just replace the bare command with itsnpxequivalent.
Download the docker-compose.yml and start the database:
curl -O https://raw.githubusercontent.com/iamvirul/wso2-docs-mcp-server/main/docker-compose.yml
docker compose up -d
Install Ollama and pull the default embedding model:
ollama pull nomic-embed-text
ollama serve
No Ollama? Skip this step. The server automatically falls back to HuggingFace ONNX - model downloads on first use with no extra setup.
DATABASE_URL="postgresql://wso2mcp:wso2mcp@localhost:5432/wso2docs" \
wso2-docs-migrate
Run migration again whenever you change
EMBEDDING_DIMENSIONS(i.e. switch embedding provider). The script detects and handles dimension changes automatically.
# Index all products (first run downloads the embedding model automatically)
DATABASE_URL="postgresql://wso2mcp:wso2mcp@localhost:5432/wso2docs" \
wso2-docs-crawl
# Index a single product (faster, great for testing)
DATABASE_URL="postgresql://wso2mcp:wso2mcp@localhost:5432/wso2docs" \
wso2-docs-crawl --product ballerina --limit 20
# Force re-index even unchanged pages
DATABASE_URL="postgresql://wso2mcp:wso2mcp@localhost:5432/wso2docs" \
wso2-docs-crawl --force
Available product IDs: apim, mi, bi, choreo, is, ballerina, library
The MCP server is launched on demand by your AI client - no background process needed.
Claude Desktop - edit ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"wso2-docs": {
"command": "wso2-docs-mcp-server",
"env": {
"DATABASE_URL": "postgresql://wso2mcp:wso2mcp@localhost:5432/wso2docs",
"EMBEDDING_PROVIDER": "ollama"
}
}
}
}
Claude Code - run once in your terminal:
claude mcp add wso2-docs \
--transport stdio \
-e DATABASE_URL="postgresql://wso2mcp:wso2mcp@localhost:5432/wso2docs" \
-e EMBEDDING_PROVIDER="ollama" \
-- wso2-docs-mcp-server
# Verify
claude mcp list
Cursor - create .cursor/mcp.json in your project root:
{
"mcpServers": {
"wso2-docs": {
"command": "wso2-docs-mcp-server",
"env": {
"DATABASE_URL": "postgresql://wso2mcp:wso2mcp@localhost:5432/wso2docs",
"EMBEDDING_PROVIDER": "ollama"
}
}
}
}
VS Code - create .vscode/mcp.json:
{
"servers": {
"wso2-docs": {
"type": "stdio",
"command": "wso2-docs-mcp-server",
"env": {
"DATABASE_URL": "postgresql://wso2mcp:wso2mcp@localhost:5432/wso2docs",
"EMBEDDING_PROVIDER": "ollama"
}
}
}
}
Using
npxinstead of global install? Replace"command": "wso2-docs-mcp-server"with"command": "npx"and add"args": ["-y", "wso2-docs-mcp-server"].
Cloud embedding provider? Add the key to
env, e.g."EMBEDDING_PROVIDER": "openai", "OPENAI_API_KEY": "sk-...".
git clone https://github.com/iamvirul/wso2-docs-mcp-server.git
cd wso2-docs-mcp-server
npm install
Install Ollama and start it:
ollama serve
No Ollama? Skip this step. The server detects Ollama is not running and automatically falls back to HuggingFace ONNX inference - the model downloads on first use with no extra setup.
cp .env.example .env
# Defaults work out of the box with Ollama.
# Only edit if using a cloud provider (OpenAI / Gemini / Voyage).
docker compose up -d
# pgAdmin available at http://localhost:5050 ([email protected] / admin)
npm run db:migrate
Note: Run migration again whenever you change
EMBEDDING_DIMENSIONS(i.e. switch embedding provider). The script detects and handles dimension changes automatically.
# Index all products
# On first run the embedding model is downloaded automatically (Ollama or HuggingFace)
npm run crawl
# Index a single product (faster, great for testing)
npm run crawl -- --product ballerina --limit 20
# Force re-index even unchanged pages
npm run crawl -- --force
npm run build
npm start
For development (no build step):
npm run dev
Replace
/ABSOLUTE/PATH/TO/wso2-docs-mcp-serverwith your actual clone path.
Claude Desktop - edit ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"wso2-docs": {
"command": "node",
"args": ["/ABSOLUTE/PATH/TO/wso2-docs-mcp-server/dist/src/index.js"],
"env": {
"DATABASE_URL": "postgresql://wso2mcp:wso2mcp@localhost:5432/wso2docs",
"EMBEDDING_PROVIDER": "ollama"
}
}
}
}
Claude Code:
claude mcp add wso2-docs \
--transport stdio \
-e DATABASE_URL="postgresql://wso2mcp:wso2mcp@localhost:5432/wso2docs" \
-e EMBEDDING_PROVIDER="ollama" \
-- node "/ABSOLUTE/PATH/TO/wso2-docs-mcp-server/dist/src/index.js"
# Verify
claude mcp list
See config-examples/claude_code.sh for a convenience script.
Cursor - create .cursor/mcp.json - see config-examples/cursor_mcp.json.
VS Code - create .vscode/mcp.json - see config-examples/vscode_mcp.json.
[
{
"title": "Deploying WSO2 API Manager",
"snippet": "WSO2 API Manager can be deployed in various topologies…",
"source_url": "https://apim.docs.wso2.com/en/latest/install-and-setup/...",
"product": "apim",
"section": "Deployment Patterns",
"score": 0.8712
}
]
The default EMBEDDING_PROVIDER=ollama runs entirely on your machine with no API key. The startup sequence is:
Is Ollama running?
├── Yes → Is model present?
│ ├── Yes → Ready (instant)
│ └── No → Pull via Ollama (streamed, runs once)
└── No → Download ONNX model from HuggingFace Hub (~250 MB, cached after first run)
and run inference in-process via @huggingface/transformers
Both paths use nomic-embed-text / Xenova/nomic-embed-text-v1 by default and produce identical 768-dim vectors, so you can switch between them without re-indexing.
When Ollama is not available, the server auto-detects the best compute backend:
Why q8 on Apple Silicon instead of CoreML/Metal?
CoreML compiles Metal shaders on first use (~20 min cold-start). For the typical chunk sizes produced by this server (6–20 chunks per page), the CPU↔GPU transfer overhead eliminates any inference gain. INT8 quantized inference on ARM NEON SIMD is consistently ~100× faster than fp32 CPU with zero cold-start cost.
Benchmark (Apple M-chip, Xenova/nomic-embed-text-v1):
fp32 CPU (before): ~1,000 ms/chunk (68 chunks ≈ 68 s of embedding)
q8 ARM NEON: ~9 ms/chunk (68 chunks ≈ 0.6 s of embedding) ← ~100× speedup
Note: For small crawls (≤ 10 pages) total wall-clock time is dominated by network I/O (HTTPS fetches to docs sites), so the end-to-end improvement is modest. The embedding speedup becomes significant at scale - crawling 500+ pages where embedding previously accounted for hours of runtime. For best crawl performance, run Ollama (
ollama serve) which parallelises inference natively and has no per-chunk overhead.
# Run a one-off re-index (checks hashes, skips unchanged pages)
npm run reindex
# Or from the project directory using node-cron (runs daily at 2 AM)
DATABASE_URL=... node -e "
const { ReindexJob } = require('./dist/jobs/reindexDocs');
const job = new ReindexJob();
job.initialize().then(() => job.scheduleDaily());
"
src/
config/ env.ts · constants.ts
vectorstore/ pgvector.ts · schema.sql
ingestion/ crawler.ts · parser.ts · githubFetcher.ts · markdownParser.ts · chunker.ts · embedder.ts
server/ mcpServer.ts · toolRegistry.ts
jobs/ reindexDocs.ts
index.ts
scripts/
crawl.ts CLI ingestion pipeline
migrate.ts Dynamic schema migration
config-examples/ claude_desktop.json · claude_code.sh · cursor_mcp.json · vscode_mcp.json
docker-compose.yml
.env.example
# Type-check
npx tsc --noEmit
# Run crawl with tsx (no build needed)
npm run crawl -- --product ballerina --limit 5
# Run server in dev mode
npm run dev
Semantic search over WSO2 docs (APIM, IS, MI, Choreo, Ballerina) via RAG and pgvector.
{ "mcpServers": { "io.github.iamvirul-wso2-docs-mcp-server": { "command": "npx", "args": [ "-y", "[email protected]" ], "env": { "DATABASE_URL": "<value>", "EMBEDDING_PROVIDER": "<value>", "OPENAI_API_KEY": "<secret>", "GEMINI_API_KEY": "<secret>", "VOYAGE_API_KEY": "<secret>" } } }}도구
아직 도구 목록이 없습니다. 원격 서버는 첫 상태 확인 후에 도구 목록이 생깁니다.
FAQ
이 페이지에서 Cursor, Claude Desktop, Claude Code 또는 VS Code용 stdio 설정을 복사합니다. PlugMCP 프록시가 아니라 게시자의 설치 방식을 사용합니다.
게시자가 등록한 환경 변수: DATABASE_URL, EMBEDDING_PROVIDER, OPENAI_API_KEY, GEMINI_API_KEY, VOYAGE_API_KEY.
Wso2 Docs은(는) stdio를 통해 로컬에서 실행됩니다.
마지막 확인: 2026-11-15. Semantic search over WSO2 docs (APIM, IS, MI, Choreo, Ballerina) via RAG and pgvector.