SEC Intelligence

io.github.jahanv01/sec-intelligence-mcp · v0.1.1

SEC 10-K/10-Q/8-K search, RAG Q&A, comparison, and anomaly detection with citations.

Source code Set it up

Connect Runs on your machine

Terminal

claude mcp add sec-intelligence-mcp -e GEMINI_API_KEY=<GEMINI_API_KEY> -e QDRANT_URL=<QDRANT_URL> -e QDRANT_API_KEY=<QDRANT_API_KEY> -e LANGFUSE_SECRET_KEY=<LANGFUSE_SECRET_KEY> -e LANGFUSE_PUBLIC_KEY=<LANGFUSE_PUBLIC_KEY> -e SEC_EDGAR_USER_AGENT=<SEC_EDGAR_USER_AGENT> -e EMBEDDING_MODEL=<EMBEDDING_MODEL> -e GEMINI_MODEL=<GEMINI_MODEL> -- uvx sec-intelligence-mcp==0.1.1

claude_desktop_config.json

{
  "mcpServers": {
    "sec-intelligence-mcp": {
      "command": "uvx",
      "args": [
        "sec-intelligence-mcp==0.1.1"
      ],
      "env": {
        "GEMINI_API_KEY": "<GEMINI_API_KEY>",
        "QDRANT_URL": "<QDRANT_URL>",
        "QDRANT_API_KEY": "<QDRANT_API_KEY>",
        "LANGFUSE_SECRET_KEY": "<LANGFUSE_SECRET_KEY>",
        "LANGFUSE_PUBLIC_KEY": "<LANGFUSE_PUBLIC_KEY>",
        "SEC_EDGAR_USER_AGENT": "<SEC_EDGAR_USER_AGENT>",
        "EMBEDDING_MODEL": "<EMBEDDING_MODEL>",
        "GEMINI_MODEL": "<GEMINI_MODEL>"
      }
    }
  }
}

~/.cursor/mcp.json

{
  "mcpServers": {
    "sec-intelligence-mcp": {
      "command": "uvx",
      "args": [
        "sec-intelligence-mcp==0.1.1"
      ],
      "env": {
        "GEMINI_API_KEY": "<GEMINI_API_KEY>",
        "QDRANT_URL": "<QDRANT_URL>",
        "QDRANT_API_KEY": "<QDRANT_API_KEY>",
        "LANGFUSE_SECRET_KEY": "<LANGFUSE_SECRET_KEY>",
        "LANGFUSE_PUBLIC_KEY": "<LANGFUSE_PUBLIC_KEY>",
        "SEC_EDGAR_USER_AGENT": "<SEC_EDGAR_USER_AGENT>",
        "EMBEDDING_MODEL": "<EMBEDDING_MODEL>",
        "GEMINI_MODEL": "<GEMINI_MODEL>"
      }
    }
  }
}

.vscode/mcp.json

{
  "servers": {
    "sec-intelligence-mcp": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "sec-intelligence-mcp==0.1.1"
      ],
      "env": {
        "GEMINI_API_KEY": "<GEMINI_API_KEY>",
        "QDRANT_URL": "<QDRANT_URL>",
        "QDRANT_API_KEY": "<QDRANT_API_KEY>",
        "LANGFUSE_SECRET_KEY": "<LANGFUSE_SECRET_KEY>",
        "LANGFUSE_PUBLIC_KEY": "<LANGFUSE_PUBLIC_KEY>",
        "SEC_EDGAR_USER_AGENT": "<SEC_EDGAR_USER_AGENT>",
        "EMBEDDING_MODEL": "<EMBEDDING_MODEL>",
        "GEMINI_MODEL": "<GEMINI_MODEL>"
      }
    }
  }
}

Replace the values in angle brackets with your own. Keys stay on your machine; we never see them.

Tools

This server runs locally, so its tools are listed by your MCP client once it starts.

Environment variables

Name	Description
`GEMINI_API_KEY` Required, secret	Google Gemini API key (free tier). Get one at https://aistudio.google.com/apikey
`QDRANT_URL` Required	Qdrant vector database URL, e.g. http://localhost:6333 for local Docker, or a Qdrant Cloud cluster URL
`QDRANT_API_KEY` Optional, secret	Only needed for a hosted Qdrant Cloud instance; leave unset for local Qdrant
`LANGFUSE_SECRET_KEY` Required, secret	LangFuse secret key for observability (free tier). Get one at https://cloud.langfuse.com
`LANGFUSE_PUBLIC_KEY` Required	LangFuse public key, paired with LANGFUSE_SECRET_KEY
`SEC_EDGAR_USER_AGENT` Optional	Identifying User-Agent for SEC EDGAR requests, e.g. "YourApp [email protected]"
`EMBEDDING_MODEL` Optional	sentence-transformers model for embeddings
`GEMINI_MODEL` Optional	Gemini model for answer generation

Details

Package	`[email protected]` (PYPI, stdio)
First published	12 Sep 2026
Last updated	12 Sep 2026
Registry name	`io.github.jahanv01/sec-intelligence-mcp`

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Local install

Installed and run on your own machine, so there is no public endpoint to check.

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