| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Generates infrastructure and architecture diagrams as Python code that compiles to visual outputs (PNG, PDF, JPG, DOT) using the Graphviz rendering engine. Wraps the diagrams library's node registry (15+ cloud providers: AWS, Azure, GCP, Kubernetes, On-Prem, SaaS) and edge styling system through MCP tool bindings, allowing Claude to construct diagram specifications that are validated and rendered server-side without requiring local Python environments.
Unique: Exposes the diagrams library's 500+ pre-built cloud provider node types and multi-provider support through MCP tool bindings, enabling Claude to generate infrastructure diagrams without requiring users to know Python or Graphviz syntax. The server-side validation and rendering approach means diagrams are generated and cached on the MCP server, not in the client.
vs alternatives: Provides native support for 15+ cloud providers with 500+ pre-built icons in a single tool, whereas generic diagram tools (Lucidchart, Draw.io) require manual icon selection and lack cloud-specific node types.
Generates flowcharts and process diagrams using a simplified 24-shape vocabulary (decision diamonds, process rectangles, start/end ovals, etc.) that compiles through the same Graphviz backend as infrastructure diagrams. Abstracts away the full diagrams library complexity by providing a focused shape set optimized for business process and workflow visualization, with support for edge labels and styling.
Unique: Provides a simplified, opinionated shape vocabulary (24 shapes) specifically for flowcharts and process diagrams, reducing the cognitive load compared to the full diagrams library. The `create_flowchart` tool abstracts away provider-specific node selection and focuses on process logic visualization.
vs alternatives: Simpler and faster than generic diagram tools for flowchart creation because it uses a curated shape set optimized for process flows, whereas tools like Lucidchart require manual shape selection from hundreds of options.
Extends infrastructure diagram generation to accept custom node icons from HTTPS URLs or local file paths, enabling organizations to use branded or proprietary icons in their diagrams. Implements client-side validation (HTTPS-only, 5MB size limit, 5-second timeout for URLs; file existence checks for local paths), automatic icon caching in ~/.diagrams_mcp/icon_cache, and format validation (PNG, JPG) before passing to the diagrams library for rendering.
Unique: Implements a dual-mode icon loading system with client-side validation, caching, and security constraints (HTTPS-only, size limits, timeout protection) that allows diagrams to incorporate custom icons without requiring users to manually embed images or modify the diagrams library. The caching layer (~/.diagrams_mcp/icon_cache) reduces repeated downloads and improves performance for frequently-used custom icons.
vs alternatives: Enables branded diagram generation with custom icons in a single tool call, whereas generic diagram tools require manual icon upload and positioning steps for each diagram.
Provides a searchable registry of 500+ available diagram nodes across 15+ cloud providers and categories (compute, database, network, storage, security, etc.). The `list_available_nodes` tool allows filtering by provider name, category, or keyword, returning node type names and metadata that can be used in diagram specifications. This enables Claude to discover available node types without requiring users to consult external documentation.
Unique: Exposes the diagrams library's internal node registry (500+ nodes across 15+ providers) as a searchable tool, allowing Claude to discover available node types dynamically without requiring external API calls or documentation lookups. The search is backed by the diagrams library's Python module introspection.
vs alternatives: Provides instant, in-context discovery of available cloud provider nodes without leaving the Claude interface, whereas users of generic diagram tools must manually browse provider documentation or icon libraries.
Validates diagram specifications before rendering by executing a dry-run that checks for syntax errors, invalid node types, missing provider definitions, and other issues without generating output files. The `validate_diagram_spec` tool parses the diagram specification, verifies all referenced nodes exist in the provider registries, and reports validation errors with actionable feedback, allowing users to fix issues before committing to full diagram generation.
Unique: Implements a pre-rendering validation step that checks diagram specifications against the diagrams library's node registries before attempting full Graphviz rendering, reducing wasted computation and providing early feedback. The dry-run approach mirrors testing patterns in infrastructure-as-code tools like Terraform.
vs alternatives: Catches diagram errors before rendering, saving time and resources compared to generic diagram tools that only report errors during final rendering.
Renders validated diagram specifications to multiple output formats (PNG, PDF, JPG, DOT) using Graphviz as the backend rendering engine. The MCP server abstracts the Graphviz command-line interface and format conversion logic, allowing Claude to specify output format as a parameter and receive the rendered diagram in the requested format without requiring knowledge of Graphviz syntax or command-line tools.
Unique: Abstracts Graphviz's multi-format rendering capabilities through MCP tool parameters, allowing Claude to request output format without requiring knowledge of Graphviz command-line syntax or format conversion tools. The server handles format-specific rendering options and optimization.
vs alternatives: Provides single-tool access to multiple output formats (PNG, PDF, JPG, DOT) without requiring separate conversion steps or external tools, whereas generic diagram tools often require manual export workflows for each format.
Supports logical grouping of diagram nodes into clusters (subgraphs) with unlimited nesting depth, enabling visual organization of infrastructure into logical domains (e.g., VPCs, namespaces, security zones). Clusters are rendered as visual containers in the output diagram, with customizable styling (colors, labels, borders). The implementation leverages Graphviz's subgraph feature, allowing Claude to specify cluster hierarchies in diagram specifications that are automatically rendered with proper visual containment.
Unique: Exposes Graphviz's subgraph clustering feature through diagram specifications, enabling unlimited nesting of logical groups without requiring users to understand Graphviz subgraph syntax. The MCP server handles cluster definition parsing and rendering.
vs alternatives: Supports unlimited cluster nesting for complex hierarchical infrastructure visualization, whereas many diagram tools limit nesting depth or require manual grouping workarounds.
Customizes connections (edges) between diagram nodes with styling options including colors, labels, and line styles (solid, dashed, dotted). Enables visual differentiation of connection types (e.g., secure vs. insecure, synchronous vs. asynchronous) and annotation of connections with labels (e.g., protocol names, bandwidth). The implementation leverages Graphviz edge attributes, allowing Claude to specify edge styling in diagram specifications that are rendered with proper visual styling.
Unique: Provides a simplified interface to Graphviz edge attributes (colors, labels, line styles) through diagram specifications, enabling visual differentiation of connection types without requiring knowledge of Graphviz syntax. The MCP server handles edge attribute parsing and rendering.
vs alternatives: Supports rich edge styling (colors, labels, line styles) in a single tool parameter, whereas generic diagram tools often require manual styling of each edge through UI interactions.
+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 Diagrams at 34/100.
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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