ai-mldeveloper-tools

SWI-Prolog

vpursuit

by vpursuit

Access SWI-Prolog for secure logic programming, ladder logic, and PLC coding. Ideal for education, puzzles, and AI logic

Provides secure access to SWI-Prolog logic programming through dual query modes with sandboxed execution, dynamic knowledge base management, and mathematical operations for educational use cases, logic puzzle solving, and AI reasoning workflows.

github stars

7

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Sandboxed execution for securityDual query modesZero setup with npx

best for

  • / Educational logic programming courses
  • / Solving logic puzzles and constraints
  • / AI reasoning workflow development
  • / Interactive Prolog development

capabilities

  • / Load and manage Prolog files
  • / Add and remove facts and rules dynamically
  • / Execute Prolog queries with deterministic pagination
  • / Run queries with true backtracking engine mode
  • / List Prolog symbols and predicates
  • / Perform mathematical operations in Prolog

what it does

Provides secure access to SWI-Prolog logic programming environment through an MCP server with sandboxed execution and dynamic knowledge base management.

about

SWI-Prolog is a community-built MCP server published by vpursuit that provides AI assistants with tools and capabilities via the Model Context Protocol. Access SWI-Prolog for secure logic programming, ladder logic, and PLC coding. Ideal for education, puzzles, and AI logic It is categorized under ai ml, developer tools.

how to install

You can install SWI-Prolog in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

BSD-3-Clause

SWI-Prolog is released under the BSD-3-Clause license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

MCP Ecosystem by vpursuit

Build Status

This monorepo contains Model Context Protocol (MCP) packages and products that enable AI assistants to work with SWI-Prolog, filesystems, and extensible plugin systems.

Products

SWI-Prolog MCP Server

Full-featured MCP server with Prolog knowledge base integration

A MCP server that lets tools-enabled LLMs work directly with SWI‑Prolog. It supports loading Prolog files, adding/removing facts and rules, listing symbols, and running queries with two modes: deterministic pagination and true engine backtracking.

Features:

  • Knowledge base management (load, assert, retract, dump)
  • Two query modes: standard (call_nth/2) and engine (true backtracking)
  • Expert Prolog assistance prompts (e.g. solving logic puzzles)
  • Comprehensive security sandboxing
  • Dynamic filesystem roots
  • Plugin-based architecture

Architecture

This repository follows a products/plugins architecture:

  • Products (products/): Published packages that end-users install (e.g., @vpursuit/swipl-mcp-server)
  • Plugins (plugins/): Internal, reusable components bundled within products (not published separately)

Internal Plugin System

The MCP server is built with a modular plugin architecture. These plugins are internal dependencies bundled into the main product:

PluginDescriptionLocation
@vpursuit/mcp-server-corePlugin system foundationplugins/server/core
@vpursuit/mcp-server-prologSWI-Prolog integrationplugins/server/prolog
@vpursuit/mcp-server-rootsFilesystem roots discoveryplugins/server/roots

Note: These plugins are marked as private in their package.json and are bundled into @vpursuit/swipl-mcp-server. They are not published separately to npm.

Plugin System for Developers

If you're developing within this monorepo, you can use the plugin system directly:

import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
import { loadPlugins } from '@vpursuit/mcp-server-core';
import { plugin as prologPlugin } from '@vpursuit/mcp-server-prolog';
import { plugin as rootsPlugin } from '@vpursuit/mcp-server-roots';

const server = new McpServer({
  name: 'my-mcp-server',
  version: '1.0.0',
});

// Load plugins
await loadPlugins(server, [prologPlugin, rootsPlugin]);

📂 Repository Structure

This is a monorepo managed with npm workspaces. Each package can be developed, tested, and published independently.

model-context-lab/
├── products/
│   └── swipl-mcp-server/    # Main MCP server (published to npm)
├── plugins/
│   └── server/
│       ├── core/            # Plugin system foundation (internal)
│       ├── prolog/          # SWI-Prolog integration (internal)
│       └── roots/           # Filesystem roots discovery (internal)
├── docs/                    # Monorepo-level documentation
├── .archive/                # Historical strategy documents
└── package.json             # Workspace configuration

Each package has:

  • Own package.json with independent versioning
  • Own README.md with complete documentation
  • Own LICENSE (BSD-3-Clause)
  • Own test suite

🔧 Development

Prerequisites

  • Node.js ≥ 20.0.0
  • SWI-Prolog (for testing Prolog integration)
  • npm ≥ 9.0.0

Setup

# Clone repository
git clone https://github.com/vpursuit/model-context-lab.git
cd model-context-lab

# Install all dependencies
npm install

# Build all packages
npm run build

# Run all tests
npm test

Working with Packages

# Build specific package
npm run build -w plugins/server/core

# Test specific package
npm test -w plugins/server/prolog

# Clean all build artifacts
npm run clean

Package Development

Each package supports:

  • npm run build - TypeScript compilation
  • npm run clean - Remove build artifacts
  • npm test - Run Vitest tests
  • npm run test:watch - Watch mode for tests

Documentation

Product Documentation (swipl-mcp-server)

Monorepo Documentation

Contributing

We welcome contributions! Please see CONTRIBUTING.md for:

  • Code of conduct
  • Development workflow
  • Testing requirements
  • Pull request process
  • Coding standards

For security issues, see SECURITY.md.

Publishing

Only products are published to npm under the @vpursuit scope:

  • Products (e.g., @vpursuit/swipl-mcp-server) are published to npm for end users
  • Plugins are internal dependencies bundled within products (not published separately)
  • Releases use semantic versioning: v<version> (e.g., v3.0.0)
  • Automated publishing via GitHub Actions
  • Supply chain security: All packages published with npm provenance attestation
  • See PUBLISHING.md for complete details

Security

All packages implement security best practices:

  • File path restrictions
  • Dangerous predicate blocking
  • Pre-execution validation
  • Timeout protection
  • Module isolation

Supply Chain Security:

  • Published with npm provenance attestation for build transparency
  • OIDC-based publishing (no long-lived tokens)
  • Cryptographically signed packages via Sigstore

See SECURITY.md for complete security documentation and reporting.

📄 License

All packages in this monorepo are licensed under BSD-3-Clause.

See LICENSE file for details.

🔗 Links

Getting Started

For end users: Install the complete MCP server

npx @vpursuit/swipl-mcp-server

For monorepo developers: Work with the plugin system

git clone https://github.com/vpursuit/model-context-lab.git
cd model-context-lab
npm install
npm run build

For contributors: Set up the development environment

git clone https://github.com/vpursuit/model-context-lab.git
cd model-context-lab
npm install
npm run build
npm test

Questions? Open an issue or see our documentation.

FAQ

What is the SWI-Prolog MCP server?
SWI-Prolog is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for SWI-Prolog?
This profile displays 34 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.634 reviews
  • Diya Srinivasan· Dec 28, 2024

    Strong directory entry: SWI-Prolog surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Diya White· Dec 24, 2024

    Useful MCP listing: SWI-Prolog is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Layla Okafor· Dec 16, 2024

    SWI-Prolog has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Piyush G· Dec 12, 2024

    Strong directory entry: SWI-Prolog surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Ira Dixit· Nov 19, 2024

    SWI-Prolog is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Yusuf Desai· Nov 7, 2024

    SWI-Prolog is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Dhruvi Jain· Nov 3, 2024

    SWI-Prolog is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Yuki Liu· Oct 26, 2024

    We wired SWI-Prolog into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Rahul Santra· Oct 22, 2024

    We evaluated SWI-Prolog against two servers with overlapping tools; this profile had the clearer scope statement.

  • Hiroshi Dixit· Oct 10, 2024

    We evaluated SWI-Prolog against two servers with overlapping tools; this profile had the clearer scope statement.

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