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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables Claude Desktop to parse natural language commands and translate them into Trello API calls for board operations. The MCP server acts as a bridge between Claude's language understanding and Trello's REST API, handling authentication via stored API credentials and routing commands to appropriate Trello endpoints. Supports creating, reading, updating, and deleting boards through conversational prompts without requiring users to interact with Trello's UI directly.
Unique: Implements MCP protocol to expose Trello operations as native Claude tools, allowing bidirectional conversation where Claude can ask clarifying questions about board operations and maintain context across multiple commands within a single session
vs alternatives: Tighter integration with Claude's reasoning than Trello's native Zapier/automation options, enabling context-aware multi-step board operations through natural conversation rather than rigid workflow rules
Translates natural language commands into CRUD operations for Trello lists and cards within boards. The MCP server maps user intents like 'add a card to the To-Do list' or 'move this card to Done' into Trello API calls that modify list membership and card properties. Handles card creation with descriptions, labels, due dates, and assignments parsed from conversational context.
Unique: Parses natural language to extract implicit card properties (due dates from phrases like 'due next Friday', labels from context keywords) without requiring structured input, reducing cognitive load on users
vs alternatives: More flexible than Trello's built-in automation rules because Claude can understand context and make decisions about card placement and properties based on conversation history rather than static conditions
Enables Claude to assign team members to cards and manage board permissions through natural language commands. The MCP server resolves team member names to Trello user IDs, assigns members to cards, and can modify board access levels. Supports querying current team members and their roles on boards.
Unique: Implements fuzzy name matching and context-aware member resolution, allowing Claude to infer team member identity from partial names or role descriptions rather than requiring exact Trello usernames
vs alternatives: Simpler than building custom permission systems while maintaining Trello's native collaboration features; Claude's reasoning enables intelligent workload balancing suggestions that static automation rules cannot provide
Allows Claude to query and retrieve board state information through natural language, including searching for specific cards, lists, and board metadata. The MCP server fetches board data from Trello's API and presents it in a format Claude can reason about, enabling context-aware operations. Supports filtering cards by labels, due dates, assigned members, and custom search criteria expressed conversationally.
Unique: Translates conversational search intent into Trello API queries, allowing Claude to understand complex filter combinations (e.g., 'cards due this week assigned to me with the bug label') without users specifying API parameters
vs alternatives: More natural than Trello's native search UI because Claude can combine multiple filter dimensions and explain results in context, whereas Trello's search requires sequential filtering steps
Enables Claude to perform coordinated operations across multiple Trello boards in a single conversation, such as copying cards between boards, syncing lists across boards, or aggregating data from multiple boards. The MCP server maintains context about multiple board states and can execute sequences of operations with transactional awareness.
Unique: Maintains conversational context across multiple board operations, allowing Claude to reason about dependencies and sequencing without requiring explicit coordination logic from the user
vs alternatives: Superior to Zapier for multi-board workflows because Claude can make intelligent decisions about which cards to sync based on content analysis rather than rigid rule-based conditions
Allows Claude to create, apply, and manage Trello labels and card metadata through conversational commands. The MCP server maps natural language label descriptions to Trello label objects, creates new labels if needed, and applies them to cards based on context. Supports managing due dates, descriptions, and other card properties through language parsing.
Unique: Parses natural language to infer label semantics and automatically creates labels if they don't exist, enabling teams to establish labeling conventions through conversation rather than manual setup
vs alternatives: More flexible than Trello's native label management because Claude can suggest label applications based on card content and maintain consistency across boards without manual enforcement
Leverages Claude's reasoning capabilities to analyze board state and provide intelligent recommendations for card organization, workload balancing, and process improvements. The MCP server retrieves board data and Claude synthesizes it into actionable suggestions based on patterns in card assignments, due dates, and labels.
Unique: Combines board data retrieval with Claude's reasoning to generate context-aware recommendations that consider team dynamics, project timelines, and implicit priorities from card metadata
vs alternatives: Provides more nuanced recommendations than Trello's built-in analytics because Claude can reason about qualitative factors (card descriptions, labels) alongside quantitative metrics (due dates, assignments)
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 Trello MCP at 24/100.
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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
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