| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a Model Context Protocol server that translates standardized MCP requests into CODESYS Scripting Engine operations through a layered architecture. The McpServer class from the MCP SDK handles protocol negotiation and request routing, while a Python script execution layer bridges MCP tool calls to CODESYS automation APIs. This enables AI clients (like Claude Desktop) to programmatically control CODESYS V3 environments without direct API knowledge.
Unique: Implements MCP as a bridge to CODESYS Scripting Engine rather than wrapping REST APIs, enabling direct automation environment control. Uses Python script templating to generate and execute CODESYS-specific automation scripts, avoiding the need for compiled CODESYS plugins.
vs alternatives: Provides standardized MCP protocol access to CODESYS where no native MCP server existed, enabling AI integration without custom REST API development or CODESYS plugin compilation.
Exposes MCP tools for complete project lifecycle operations: open_project, create_project, save_project, and compile_project. Each tool translates MCP parameters into Python scripts that invoke CODESYS Scripting Engine APIs to manipulate project files, manage in-memory project state, and trigger compilation with error reporting. The toolkit maintains awareness of the currently open project context across multiple tool invocations within a single MCP session.
Unique: Manages CODESYS project state across multiple MCP tool invocations within a single session, maintaining context of the currently open project. Uses Python script generation to invoke CODESYS Scripting Engine APIs directly, avoiding the need for external build tools or command-line compilers.
vs alternatives: Provides programmatic project management without requiring CODESYS GUI interaction or external build system integration, enabling seamless AI-driven automation workflows.
Implements MCP tools for creating and modifying POUs (Programs, Function Blocks, Functions) with separate declaration and implementation code sections. The create_pou tool generates new POUs with specified type and initial code, while set_pou_code updates existing POU code. The toolkit reads POU code through MCP resources (codesys://project/{path}/pou/{pou_path}/code) that parse CODESYS project XML to extract declaration and implementation sections separately, enabling AI systems to understand and modify POU structure.
Unique: Separates POU declaration and implementation code into distinct read/write operations, enabling AI systems to understand and modify POU interfaces independently from implementation logic. Uses CODESYS project XML parsing to extract code sections without requiring CODESYS GUI interaction.
vs alternatives: Provides structured POU code access and generation where CODESYS GUI requires manual editing, enabling programmatic code generation and analysis for AI-assisted development.
Exposes MCP tools create_property and create_method for adding properties and methods to Function Blocks. These tools generate Python scripts that invoke CODESYS Scripting Engine APIs to add typed properties (with getter/setter code) and methods (with parameters and return types) to existing Function Blocks. The toolkit handles the code generation for property accessors and method stubs, reducing boilerplate for AI-assisted development.
Unique: Automates property and method stub generation for Function Blocks through MCP tools, reducing manual boilerplate while maintaining CODESYS Scripting Engine compatibility. Generates getter/setter code patterns automatically rather than requiring manual implementation.
vs alternatives: Provides programmatic Function Block interface scaffolding where CODESYS GUI requires manual property/method creation, enabling faster AI-assisted development of complex Function Block hierarchies.
Implements MCP resource endpoints (codesys://project/{path}/structure) that parse CODESYS project XML files to expose hierarchical project structure as queryable resources. The toolkit extracts object metadata (POUs, properties, methods, variables) from the project file and returns structured JSON representations without requiring CODESYS GUI interaction. This enables AI clients to understand project topology for code generation, refactoring, or analysis tasks.
Unique: Parses CODESYS project XML directly to expose structure as MCP resources without requiring CODESYS GUI or Scripting Engine execution, enabling fast read-only access to project metadata. Returns hierarchical JSON representation suitable for AI context and code generation planning.
vs alternatives: Provides fast, read-only project structure access without CODESYS process overhead, enabling AI systems to understand project topology for informed code generation decisions.
Implements MCP resource endpoint (codesys://project/status) that queries CODESYS Scripting Engine state and returns current project status, including whether a project is open, the current project file path, unsaved changes flag, and scripting engine availability. This resource is generated by executing a Python script that invokes CODESYS Scripting Engine APIs to introspect runtime state, enabling MCP clients to determine system readiness before executing project operations.
Unique: Provides real-time CODESYS Scripting Engine status through MCP resources by executing Python scripts that query engine APIs, enabling clients to detect system readiness without direct CODESYS process access. Returns structured status object suitable for conditional workflow logic.
vs alternatives: Enables MCP clients to verify CODESYS availability and project state before executing operations, preventing failed automation attempts and improving error handling in CI/CD pipelines.
Implements a Python script templating system that generates CODESYS Scripting Engine automation scripts from MCP tool parameters. The toolkit maintains Python script templates for each operation (project management, POU creation, compilation) that are populated with parameters and executed via subprocess calls. This approach decouples MCP protocol handling from CODESYS-specific logic, enabling easy extension with new operations and version-specific script variants.
Unique: Uses Python script templating to generate and execute CODESYS Scripting Engine operations, enabling version-specific automation without modifying core MCP server code. Decouples protocol handling from CODESYS-specific logic through subprocess-based execution.
vs alternatives: Provides extensible automation through script templates rather than compiled plugins, enabling rapid addition of new CODESYS capabilities and support for multiple CODESYS versions without recompilation.
Supports configuration and management of multiple CODESYS installations through environment variables and configuration files. The toolkit can target different CODESYS versions or instances by specifying installation paths, enabling users to work with multiple CODESYS environments through a single MCP server. Configuration is managed via command-line options and environment variables that are passed to Python scripts for installation-specific scripting engine access.
Unique: Enables single MCP server to target multiple CODESYS installations through configuration-based installation path management, allowing teams to work with heterogeneous CODESYS environments without separate server instances per version.
vs alternatives: Provides flexible multi-installation support through configuration rather than requiring separate MCP server instances, simplifying deployment for teams with multiple CODESYS versions.
+2 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Codesys-mcp-toolkit at 22/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities