AI and Logos API

Alvin Tam

I asked Grok how to develop an app using MCP and get access to the LLMs to ask queries using the resources in Logos. Here is its response:

Developing an app using the Model Context Protocol (MCP) to query Large Language Models (LLMs) with resources from your Logos Bible Software account is an exciting project that combines AI standardization with biblical scholarship. Logos Bible Software is a powerful tool for biblical study, offering access to a vast library of commentaries, lexicons, original language texts, and more. MCP can streamline the integration of these resources into an AI-driven app. Below, I’ll outline a step-by-step process to build such an app, assuming you have a Logos account and some development experience.

App Concept

Let’s design a Bible Study Assistant app:

• Purpose: Users ask questions (e.g., “What does ‘agape’ mean in John 3:16?”) and the app uses an LLM to provide answers based on Logos resources.
• Features: Pulls data from Logos (e.g., Greek lexicons, commentaries), feeds it to an LLM via MCP, and returns a natural language response.

Development Process Using MCP

1. Prerequisites

• Logos Bible Software Account: Ensure you have an active account with access to resources (e.g., lexicons, Bibles, commentaries).
• Development Tools: Basic knowledge of Python or TypeScript (MCP SDKs are available in both), Git, and an IDE (e.g., VS Code).
• LLM Access: Use an MCP-compatible LLM host like Claude Desktop (from Anthropic) or another model with an MCP client (e.g., Cursor or Windsurf).
• MCP Setup: Familiarity with MCP’s client-server model (see my previous response on MCP for details).

2. Access Logos Resources

Logos doesn’t provide a public API for direct resource access, but you can work with its data in two practical ways:

• Manual Export: Export relevant resources (e.g., commentaries, word studies) as text files or PDFs from your Logos library. This is viable for a small, static dataset.
• Local Files: If you’ve downloaded Logos resources to your computer, they’re often stored as proprietary files. You’d need to extract text manually or use Logos’ “Copy to Clipboard” feature to gather data.

For a dynamic app, you’d ideally want an API, but since Logos doesn’t offer one publicly, we’ll assume you’re working with exported text files (e.g., a lexicon file or commentary excerpts) stored locally.

3. Set Up MCP Components

• MCP Host: Your app will act as the host, using an LLM (e.g., Claude Desktop) to process queries. Install Claude Desktop or another MCP-compatible IDE like Windsurf.
• MCP Client: Embedded in your app, this connects to your MCP server. Use Anthropic’s Python or TypeScript SDK to configure it.
• MCP Server: Build a custom server to expose Logos resources and tools. This server will:
  • Serve Resources: Logos data (e.g., text files of lexicons or commentaries).
  • Provide Tools: Functions like searching for a verse or term in the data.
  • Offer Prompts: Templates to guide the LLM (e.g., “Explain [term] using [resource]”).

Here’s a basic setup in Python using the MCP SDK:

from mcp import Server, Resource, Tool

# Initialize MCP Server

server = Server(name="LogosBibleServer", version="1.0")

# Add a Resource (e.g., lexicon data)

server.add_resource(

    uri="file:///logos/lexicon.txt",

    name="Greek Lexicon",

    description="Greek word definitions",

    mime_type="text/plain",

    read_handler=lambda request: Resource(contents=open("lexicon.txt").read())

)

# Add a Tool (e.g., search for a term)

server.add_tool(

    name="search_logos",

    description="Search Logos resources for a term or verse",

    parameters={"query": "string"},

    handler=lambda params: search_logos_data(params["query"])

)

# Start the server

server.start(transport="stdio")  # Use stdio for local testing

• search_logos_data: A custom function you’d write to search your exported Logos files (e.g., using regex or a simple text search).

4. Connect Logos Data to the LLM

• Export Data: Save Logos resources (e.g., a Greek lexicon or John commentary) as .txt files in a local directory.
• Index the Data: For better performance, preprocess the data into a searchable format (e.g., a JSON file with terms and definitions).
• Feed via MCP: The MCP server exposes this data as resources. When a user queries “What does ‘agape’ mean in John 3:16?”, the client:
  1. Requests the lexicon resource and John 3:16 commentary from the server.
  2. Sends this data to the LLM with a prompt like: “Using the provided lexicon and commentary, explain ‘agape’ in John 3:16.”

Sample client request:

{

  "jsonrpc": "2.0",

  "id": 1,

  "method": "resources/read",

  "params": {"uri": "file:///logos/lexicon.txt"}

}

The server returns the text, which the LLM processes.

5. Craft MCP Prompts

Define a reusable prompt to ensure the LLM uses Logos data correctly:

server.add_prompt(

    name="explain_term",

    template="Using the provided resource '{resource}', explain the meaning of '{term}' in the context of '{verse}'.",

    parameters={"term": "string", "verse": "string", "resource": "string"}

)

The client can invoke this prompt, filling in “agape,” “John 3:16,” and “Greek Lexicon.”

6. Build the App Interface

• Simple UI: Create a basic interface (e.g., with Flask or React) where users input queries.
• Query Flow:
  1. User types: “What does ‘agape’ mean in John 3:16?”
  2. App’s MCP Client sends the query to the server to fetch relevant Logos data.
  3. Client passes the data and prompt to the LLM host (e.g., Claude).
  4. LLM returns: “In John 3:16, ‘agape’ (ἀγάπη) refers to unconditional, sacrificial love, as defined in the lexicon. The commentary notes its use here emphasizes God’s love for humanity.”
  5. App displays the response.

7. Test and Refine

• Test Locally: Run the MCP server and host on your machine. Use sample queries to ensure the LLM uses Logos data accurately.
• Expand Resources: Add more Logos exports (e.g., Hebrew lexicons, theological dictionaries) as needed.
• Optimize: If the dataset grows, consider a local database (e.g., SQLite) and a tool to query it via MCP.

Challenges and Solutions

• No Logos API: Since Logos lacks a public API, you’re limited to exported data. For a robust app, you’d need to periodically update your local files.
• Data Volume: Logos resources are extensive. Start with a subset (e.g., key lexicons or commentaries) to keep it manageable.
• Security: MCP’s local-first design keeps data on your machine, but ensure user consent for tool execution (per MCP guidelines).

Example Output

User Query: “What does ‘agape’ mean in John 3:16?”
App Response: “Based on the Greek Lexicon from Logos, ‘agape’ (ἀγάπη) means a selfless, unconditional love. In John 3:16, it describes God’s sacrificial love for the world, as supported by [commentary name], which highlights its divine, redemptive nature.”

Next Steps

• Get Started: Install the MCP Python SDK (pip install mcp), export some Logos data, and adapt the code above.
• Enhance: Add tools for cross-references or original language parsing.
• Scale: If Logos ever releases an API, adapt your MCP server to query it directly.

This app leverages MCP’s power to unify Logos resources with LLM intelligence, offering a scalable, standardized way to enhance Bible study. Want to dive deeper into any step? Let me know!