openviking-mcp
Set up and run the OpenViking MCP server for RAG capabilities. Use when users need semantic search and document Q&A exposed through Model Context Protocol for Claude Desktop/CLI or other MCP clients. Triggers on requests about OpenViking MCP, RAG servers, or semantic search MCP setup.
Install
mkdir -p .claude/skills/openviking-mcp && curl -L -o skill.zip "https://mcp.directory/api/skills/download/6973" && unzip -o skill.zip -d .claude/skills/openviking-mcp && rm skill.zipInstalls to .claude/skills/openviking-mcp
About this skill
OpenViking MCP Server
HTTP MCP server that exposes OpenViking RAG capabilities as tools for Claude and other MCP clients.
What It Provides
| Tool | Purpose |
|---|---|
query | Full RAG pipeline — semantic search + LLM answer generation |
search | Semantic search only, returns matching documents with scores |
add_resource | Ingest files, directories, or URLs into the database |
Prerequisites
- Python 3.13+
uvinstalled (curl -LsSf https://astral.sh/uv/install.sh | sh)- OpenAI API key (for LLM and embeddings)
Setup Steps
Step 1: Get the Code
Clone the OpenViking repository:
git clone https://github.com/ZaynJarvis/openviking.git
# Or your fork/organization's repo
cd openviking/examples/mcp-query
Step 2: Install Dependencies
uv sync
Step 3: Configure API Keys (Human Input Required)
Copy the example config:
cp ov.conf.example ov.conf
You must edit ov.conf and add your API tokens. The critical fields:
| Field | Purpose | Example |
|---|---|---|
vlm.token | LLM for generating answers | sk-... (OpenAI) |
embedding.token | Embeddings for semantic search | sk-... (OpenAI) |
Wait for user to confirm: Ask the user to paste their ov.conf (with tokens redacted if sharing logs) or confirm they've set it up before proceeding.
Example minimal config:
{
"vlm": {
"provider": "openai",
"model": "gpt-4o-mini",
"token": "YOUR_OPENAI_API_KEY"
},
"embedding": {
"provider": "openai",
"model": "text-embedding-3-small",
"token": "YOUR_OPENAI_API_KEY"
}
}
Step 4: Start the Server
uv run server.py
Server runs at http://127.0.0.1:8000/mcp by default.
Step 5: Connect to Claude
Claude CLI:
claude mcp add --transport http openviking http://localhost:8000/mcp
Claude Desktop: Add to ~/.mcp.json:
{
"mcpServers": {
"openviking": {
"type": "http",
"url": "http://localhost:8000/mcp"
}
}
}
Server Options
uv run server.py [OPTIONS]
--config PATH Config file path (default: ./ov.conf)
--data PATH Data directory path (default: ./data)
--host HOST Bind address (default: 127.0.0.1)
--port PORT Listen port (default: 8000)
--transport TYPE streamable-http | stdio (default: streamable-http)
Environment variables: OV_CONFIG, OV_DATA, OV_PORT, OV_DEBUG
Usage Examples
Once connected, Claude can use these tools:
Query with RAG:
"Search my documents for information about Q3 revenue and summarize the findings"
Semantic search only:
"Find documents related to machine learning architecture"
Add documents:
"Index the PDF at ~/documents/report.pdf"
"Add https://example.com/article to my knowledge base"
Troubleshooting
| Issue | Solution |
|---|---|
| Port in use | Change with --port 9000 |
| Config not found | Ensure ov.conf exists or set OV_CONFIG path |
| Dependencies missing | Run uv sync in the mcp-query directory |
| Authentication errors | Check your API tokens in ov.conf |
Resources
- OpenViking repo:
code/openviking/or https://github.com/ZaynJarvis/openviking
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