⚠

High Risk:This skill has significant security concerns. Review the findings below before installing.

openviking-mcp

⚠Caution·Scanned 2/19/2026

Dangerous skill: instructs running a remote install script curl -LsSf https://astral.sh/uv/install.sh | sh and local commands like uv run server.py. It also directs storing and using API tokens in ov.conf and exposes http://127.0.0.1:8000/mcp while referencing OV_CONFIG, OV_DATA, OV_PORT, and OV_DEBUG.

from clawhub.ai·v1d7fdd2·3.5 KB·0 installs

Scanned from 0.1.2 at 1d7fdd2 · Transparency log ↗

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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+
uv installed (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