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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Execute SQL queries against Tinybird's serverless ClickHouse infrastructure through MCP protocol, with automatic connection pooling and query optimization. The MCP server translates tool calls into authenticated HTTP requests to Tinybird's API endpoints, handling response serialization and error propagation back to the LLM client.
Unique: MCP-native integration that exposes Tinybird as a tool directly to LLM clients without requiring custom API wrapper code or middleware — the MCP server handles schema discovery, authentication token management, and response formatting natively
vs alternatives: Tighter integration than REST API wrappers because MCP protocol enables Claude to discover available queries and parameters automatically, reducing context overhead and enabling more natural agentic interaction with analytics data
Create, configure, and manage data sources (connectors) that feed data into Tinybird's ClickHouse backend through the MCP interface. The MCP server exposes Tinybird's data source API, allowing LLM clients to define ingestion pipelines for CSV, JSON, Parquet, and streaming sources without leaving the conversation context.
Unique: Exposes Tinybird's full data source API through MCP, enabling LLM agents to programmatically define and manage data pipelines — most analytics tools require UI-based configuration, but this MCP server treats data ingestion as a first-class tool callable by Claude
vs alternatives: More flexible than Tinybird's web UI for automation because agents can dynamically create data sources based on runtime conditions, whereas manual UI configuration is static and non-programmable
Create and manage Tinybird Pipes (data transformation DAGs) and materialized views through MCP tool calls, enabling LLM clients to define multi-step analytics workflows. The MCP server translates high-level transformation requests into Tinybird's Pipe DSL, handles dependency resolution, and triggers incremental materialization.
Unique: Abstracts Tinybird's Pipe DSL into MCP tool calls, allowing Claude to reason about data transformations at a higher level than raw SQL — the MCP server handles syntax generation, validation, and dependency ordering
vs alternatives: More accessible than writing Pipes manually because LLM clients can describe transformations in natural language and the MCP server generates valid Pipe definitions, reducing syntax errors and enabling non-expert users to build complex pipelines
Publish Tinybird Pipes and queries as REST API endpoints through MCP, and discover available endpoints with their schemas and authentication requirements. The MCP server manages endpoint creation, parameter binding, and response formatting, exposing them as callable tools that Claude can invoke or recommend to users.
Unique: Treats API endpoint creation as a first-class MCP tool, allowing Claude to publish analytics queries as REST APIs on-demand — most analytics platforms require manual API configuration, but this MCP server enables programmatic endpoint management
vs alternatives: More agile than manual API configuration because agents can publish new endpoints in response to user requests, whereas traditional approaches require engineering effort to expose each new query
Query Tinybird workspace metadata including available tables, columns, Pipes, data sources, and API endpoints through MCP tools. The MCP server introspects the Tinybird workspace schema and exposes it as structured data, enabling Claude to understand the available analytics assets and make informed decisions about which queries or transformations to execute.
Unique: Exposes Tinybird workspace metadata as MCP tools, enabling Claude to dynamically discover available assets and make context-aware decisions about which queries to execute — most analytics tools require manual documentation or UI exploration
vs alternatives: Enables more intelligent agentic behavior than static documentation because Claude can query workspace structure in real-time and adapt its recommendations based on actual available data, reducing hallucination about non-existent tables or columns
Manage Tinybird API authentication through MCP by storing and rotating API tokens, handling token expiration, and managing workspace-level permissions. The MCP server securely stores credentials and injects them into all Tinybird API requests, abstracting authentication complexity from the LLM client.
Unique: Centralizes Tinybird authentication at the MCP server level, preventing API tokens from being exposed in LLM context or conversation logs — the server injects credentials into all requests transparently
vs alternatives: More secure than passing API tokens to Claude directly because credentials never enter the LLM context, reducing the attack surface for token leakage or accidental exposure in logs
Format and export query results from Tinybird in multiple formats (JSON, CSV, Parquet) through MCP tools, with support for result pagination, filtering, and aggregation. The MCP server handles result serialization and can stream large result sets to avoid token overhead in LLM context.
Unique: Provides flexible result formatting through MCP tools rather than forcing JSON-only responses, enabling Claude to export results in formats optimized for specific downstream consumers
vs alternatives: More flexible than Tinybird's native API responses because the MCP server can transform results on-the-fly into CSV, Parquet, or other formats without requiring separate client-side processing
Validate SQL queries before execution and provide detailed error messages when queries fail, including suggestions for fixing syntax errors or schema mismatches. The MCP server parses queries against the workspace schema and returns actionable error feedback to Claude, enabling iterative query refinement.
Unique: Provides pre-execution query validation through MCP, catching errors before they consume Tinybird compute resources — most analytics tools only report errors after query execution
vs alternatives: Reduces wasted compute and iteration time compared to blind query submission because Claude receives validation feedback immediately and can refine queries before execution
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 Tinybird at 21/100.
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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