Model Context Lab
This is a monorepo containing Model Context Protocol (MCP) software packages and products, mainly providing a fully functional MCP server that enables AI assistants to interact with SWI - Prolog, the file system, and an extensible plugin system.
rating : 2.5 points
downloads : 6.2K
What is the SWI-Prolog MCP Server?
This is a bridge server that connects AI assistants with the SWI-Prolog logic programming language. It is built based on the Model Context Protocol (MCP) standard, allowing AI tools such as Claude and Cursor to directly interact with Prolog. AI can read Prolog code files, add new logical rules, query the knowledge base, and perform complex logical reasoning through this server.How to use the SWI-Prolog MCP Server?
It's very simple to use: 1) Install Node.js and SWI-Prolog, 2) Install the server via npm, 3) Configure the MCP connection in the AI tool, 4) The AI can start using Prolog. The server will automatically discover Prolog files in the project and provide functions such as loading, querying, and modifying.Applicable scenarios
This server is particularly suitable for scenarios such as solving logical puzzles, developing rule systems, building knowledge bases, learning Prolog in education, performing automated reasoning tasks, data validation, and constraint solving. Whether it's AI-assisted programming or automated logical reasoning, it can leverage the powerful capabilities of Prolog.Main features
Knowledge base management
Complete operations on the Prolog knowledge base: load.pl files, dynamically add facts and rules, delete unnecessary rules, and export the current state of the knowledge base. AI can manage the Prolog knowledge base just like managing a database.
Dual-mode query
Two query methods are provided: standard mode (return results in pages) and engine mode (the real Prolog backtracking mechanism). The standard mode is suitable for obtaining all solutions, and the engine mode is suitable for exploratory queries and complex reasoning.
Expert-level Prolog assistant
Built - in AI prompt templates specifically for Prolog programming to help AI better understand how to write Prolog code, solve logical puzzles, optimize query performance, etc.
Security sandbox
Comprehensive security protection: restrict file access paths, block dangerous predicates, perform pre - execution verification, provide timeout protection, and implement module isolation. Ensure that AI operations are safe and controllable.
Dynamic file system discovery
Automatically discover Prolog files in the project, supporting multi - directory configuration. AI can access project - related files but cannot access system files beyond its authority.
Plugin architecture
Modular design, with core functions implemented through plugins. It is convenient to expand new functions, and the code structure is clear and easy to maintain.
Advantages
Enable AI to acquire logical programming capabilities and handle tasks such as rule reasoning and constraint solving that traditional AI is not good at
Based on the MCP standard, compatible with all AI tools that support MCP (Claude Desktop, Cursor, etc.)
Complete knowledge base lifecycle management, allowing AI to continuously learn and modify Prolog rules
Enterprise - level security design to prevent AI misoperations or malicious code execution
Open - source and free, with active community support and continuous updates
Limitations
Requires users to install SWI - Prolog locally, increasing the deployment complexity
The Prolog learning curve is relatively steep, and AI may need time to adapt to the logical programming paradigm
Performance is limited by the local Prolog engine, and large - scale knowledge bases may respond slowly
Currently mainly targeted at developers and technical users, and non - technical users need guidance
How to use
Installation prerequisites
Ensure that the system has installed: Node.js (≥20.0.0), SWI - Prolog, npm (≥9.0.0). Windows users need to add SWI - Prolog to the system PATH.
Install the MCP server
Install the SWI - Prolog MCP server globally via npm or run it directly using npx.
Configure the AI tool
Configure the MCP server connection in the AI tool you are using. The configuration methods vary slightly for different tools.
Start using
Restart the AI tool. Now you can ask the AI Prolog - related questions. The AI will automatically use the MCP server to interact with Prolog.
Usage examples
Family relationship reasoning
The AI helps users analyze family relationships and find specific relationship chains.
Solving logical puzzles
Use Prolog to solve logical constraint problems such as Einstein's riddle.
Business rule verification
Verify whether the data complies with complex business rules.
Learning Prolog programming
The AI serves as a Prolog programming assistant to help learn and debug code.
Frequently Asked Questions
Do I need to know Prolog to use this server?
Is this server secure? Will the AI delete my files?
Which AI tools are supported?
Will querying a large - scale knowledge base be slow?
How can I contribute code or report issues?
Do I need a license for commercial use?
Related resources
GitHub repository
Complete source code, issue tracking, and discussion area
NPM package page
Installation package, version history, and download statistics
SWI - Prolog official website
Prolog language documentation, tutorials, and downloads
Model Context Protocol
MCP protocol standards, specifications, and other servers
Detailed feature documentation
Advanced features, architecture, and deployment guides
Prolog learning resources
Free online Prolog tutorials, suitable for beginners

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