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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Automatically discovers and processes documents across multiple formats (.pdf, .xls, .docx, .txt, .md, .csv) from a configured local directory tree, extracting text content and preparing it for embedding generation. Uses recursive folder traversal to handle nested directory structures without manual file selection, enabling hands-off indexing of large document collections.
Unique: Implements recursive folder scanning with automatic format detection and unified text extraction pipeline, eliminating need for manual file selection or format-specific workflows — all documents in a directory tree are indexed in a single operation without user intervention
vs alternatives: More comprehensive than Pinecone or Weaviate (which require manual document uploads) and more privacy-preserving than cloud RAG solutions like LangChain Cloud, since all processing stays on-premises
Generates dense vector embeddings for document chunks using Sentence Transformers (BAAI models by default), converting text into high-dimensional vectors suitable for semantic similarity search. Supports model selection via environment configuration, allowing users to choose embeddings optimized for their domain (e.g., multilingual, domain-specific fine-tuned models) without code changes.
Unique: Provides environment-variable-based model selection (EMBEDDING_MODEL_ID) allowing runtime switching between Sentence Transformer models without code changes, combined with configurable embedding dimensions (EMBEDDING_SIZE) for memory/accuracy tradeoffs — more flexible than hardcoded embedding pipelines
vs alternatives: More privacy-preserving than OpenAI embeddings API (no data leaves premises) and more cost-effective than cloud embedding services for large-scale indexing, though slower than GPU-accelerated cloud solutions
Stores generated embeddings in Qdrant vector database and performs approximate nearest neighbor (ANN) search to retrieve semantically similar documents for a given query. Uses vector similarity metrics (cosine, Euclidean) to rank documents by relevance without keyword matching, enabling natural language search across document collections.
Unique: Integrates Qdrant as the vector store backend with configurable similarity metrics and optional reranking pipeline, providing both fast approximate search and relevance refinement — architecture separates retrieval (ANN) from ranking (reranker) for modularity
vs alternatives: More privacy-preserving than Pinecone (fully on-premises) and more flexible than Weaviate (supports multiple embedding models and rerankers), though requires manual Qdrant deployment vs managed vector databases
Applies a second-stage ranking model (typically BAAI cross-encoder) to refine the top-k results from vector search, re-scoring documents based on semantic relevance to the original query. This two-stage retrieval pattern (retrieve-then-rerank) improves precision by filtering out false positives from the initial ANN search without requiring full dataset re-scoring.
Unique: Implements two-stage retrieval (ANN + cross-encoder reranking) as an optional pipeline stage, allowing users to trade latency for precision — reranker is applied only to top-k results, avoiding full-dataset re-scoring cost
vs alternatives: More cost-effective than reranking all documents and more effective than single-stage vector search alone; similar to Cohere's reranking API but fully on-premises with no API calls or data transmission
Abstracts LLM interaction behind a provider interface supporting Ollama (local), OpenAI (ChatGPT), and Anthropic (Claude) without code changes. Uses environment configuration to select the active LLM backend, enabling users to switch between fully local inference and cloud LLMs based on deployment mode, privacy requirements, or cost considerations.
Unique: Implements provider abstraction pattern allowing runtime LLM selection via environment variables (LLM_PROVIDER, OLLAMA_BASE_URL, OPENAI_API_KEY, ANTHROPIC_API_KEY) without code changes — supports three distinct deployment modes (fully local, hybrid with OpenAI, hybrid with Anthropic) from single codebase
vs alternatives: More flexible than LangChain (which requires code changes to swap providers) and more privacy-preserving than cloud-only solutions like OpenAI's RAG; enables cost optimization by using local Ollama for development and ChatGPT for production
Exposes Minima's RAG capabilities as a Model Context Protocol (MCP) server, allowing external LLM clients (Claude Desktop, other MCP-compatible applications) to invoke document search and retrieval as remote tools. Implements MCP's request-response protocol for tool discovery, invocation, and result streaming without requiring direct API integration.
Unique: Implements full MCP server protocol stack enabling Claude Desktop and other MCP clients to invoke RAG search as a remote tool — architecture separates MCP transport layer from core RAG logic, allowing tool-agnostic document retrieval
vs alternatives: More seamless than REST API integration (MCP handles tool discovery and schema automatically) and more privacy-preserving than cloud RAG tools, though requires MCP client support vs universal HTTP API compatibility
Provides dual user interfaces for document search and RAG interaction: a web-based UI (accessible via browser) and a native Electron desktop application. Both interfaces connect to the same backend services (indexer, vector database, LLM) and support chat-style interaction with retrieved context, enabling non-technical users to search documents without CLI or API knowledge.
Unique: Provides parallel web and Electron interfaces sharing the same backend, allowing users to choose between browser-based access and native desktop application — both support chat-style RAG interaction with retrieved context display
vs alternatives: More user-friendly than CLI-only tools like LlamaIndex and more accessible than API-only solutions; Electron app provides offline-capable desktop experience vs web-only competitors
Centralizes all system configuration through environment variables (.env file), including document paths, embedding models, vector database endpoints, LLM providers, and API keys. Eliminates need for code changes when switching deployment modes, models, or providers — configuration is purely declarative and environment-specific.
Unique: Uses environment variables for all configuration (LOCAL_FILES_PATH, EMBEDDING_MODEL_ID, EMBEDDING_SIZE, LLM_PROVIDER, OLLAMA_BASE_URL, OPENAI_API_KEY, ANTHROPIC_API_KEY) enabling complete deployment flexibility without code changes — supports three distinct deployment modes from single codebase via configuration alone
vs alternatives: Simpler than YAML/JSON config files for containerized deployments and more flexible than hardcoded defaults; follows 12-factor app principles for cloud-native applications
+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 Minima 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