| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a three-tier data model where Projects contain Tasks and Knowledge entities as distinct node types in Neo4j, with relationship edges defining containment and dependency chains. Uses Cypher query language for traversal and aggregation across the hierarchy, enabling agents to structure complex workflows with nested task dependencies and associated knowledge artifacts without flattening the organizational structure.
Unique: Uses Neo4j as the primary persistence layer with a three-tier node schema (Project, Task, Knowledge) rather than relational tables or document stores, enabling agents to reason about complex dependency graphs and perform relationship-aware queries without JOIN operations or denormalization.
vs alternatives: Outperforms relational databases for deep hierarchical queries and dependency traversal; more structured than document stores (MongoDB) for maintaining strict entity relationships and enabling graph-based reasoning by LLM agents.
Exposes project, task, and knowledge management operations as MCP tools with standardized input schemas and response formatting. Each tool (create, read, update, delete, list) maps to Neo4j service methods that validate inputs via Zod schemas, execute Cypher mutations/queries, and return structured JSON responses. Tools are discoverable by MCP clients and include detailed descriptions for LLM agent planning.
Unique: Implements MCP tools as a first-class integration pattern rather than REST endpoints or direct database access, allowing LLM agents to discover and invoke project/task/knowledge operations through the standard MCP protocol with automatic schema validation and response formatting.
vs alternatives: Simpler for LLM agents than REST APIs because tool schemas are self-documenting and validated by the MCP framework; more secure than direct database access because all operations go through typed tool handlers with input validation.
Implements consistent error handling with typed error classes (ValidationError, NotFoundError, DatabaseError, etc.) and structured logging using Winston or Pino. All errors include context (request ID, operation type, entity ID) and are logged with appropriate severity levels. HTTP responses include error codes and messages; MCP responses include error details in the response object.
Unique: Uses typed error classes and structured logging with request context propagation, enabling correlation of errors across multiple operations and layers without manual context threading.
vs alternatives: More informative than generic error messages because errors include context (request ID, entity ID, operation type); more actionable than unstructured logs because errors are categorized by type and severity.
Uses Zod to validate and parse environment variables at startup, ensuring all required configuration is present and correctly typed before the server starts. Supports configuration for database connection, server ports, authentication secrets, logging levels, and feature flags. Provides clear error messages if configuration is invalid or missing.
Unique: Validates all configuration at startup using Zod schemas, preventing the server from starting with invalid or missing configuration and providing clear error messages for misconfiguration.
vs alternatives: More robust than manual configuration parsing because Zod enforces type safety and constraints; faster to debug than runtime configuration errors because validation happens at startup.
Provides a single search interface that queries across all three entity types (Projects, Tasks, Knowledge) using Neo4j full-text indexes and optional semantic search via embeddings. Accepts a search query string, executes Cypher queries against indexed properties, and returns ranked results grouped by entity type with relevance scores. Supports filtering by project, status, and other metadata.
Unique: Unifies search across three distinct entity types (Projects, Tasks, Knowledge) in a single query using Neo4j's full-text index capabilities, with optional semantic search layer for conceptual matching beyond keyword overlap.
vs alternatives: More efficient than separate searches per entity type; leverages Neo4j's native indexing rather than external search engines (Elasticsearch), reducing operational complexity for small-to-medium deployments.
Implements a research workflow where an LLM agent iteratively formulates research questions, searches the knowledge base and external sources, synthesizes findings, and refines queries based on results. The tool manages conversation history, tracks research progress, and stores findings back into the Knowledge tier. Uses chain-of-thought reasoning to decompose complex research goals into sub-questions.
Unique: Implements research as an iterative, agent-driven process with feedback loops where the LLM refines search queries based on findings, rather than a single-shot search-and-summarize pattern. Integrates findings back into the Neo4j knowledge base as structured entities.
vs alternatives: More thorough than simple search-and-summarize because it enables agents to reason about gaps and refine queries; more autonomous than manual research because the agent drives the iteration loop without human intervention.
Exposes projects, tasks, and knowledge items as MCP resources (read-only data endpoints) that clients can subscribe to for real-time updates or fetch on-demand. Resources are formatted as text or JSON and include metadata about the entity, relationships, and child entities. Enables agents to maintain context about the current project/task state without invoking tools.
Unique: Implements MCP resources as a separate read-only interface alongside tools, allowing agents to fetch and subscribe to entity state without invoking mutation operations. Resources include relationship context (child tasks, associated knowledge) in a single fetch.
vs alternatives: More efficient than tool-based reads for context maintenance because resources can be cached and subscribed to; cleaner separation of concerns than mixing read/write in tools.
Maintains a request context (trace ID, agent ID, operation type) throughout the lifecycle of MCP operations, enabling correlation of related database mutations and tool invocations. Uses Node.js AsyncLocalStorage to propagate context without explicit parameter passing. Logs all operations with context metadata for debugging and audit trails.
Unique: Uses AsyncLocalStorage to propagate request context implicitly through the call stack, avoiding the need to thread context through every function signature. Enables correlation of distributed operations without explicit parameter passing.
vs alternatives: Cleaner than manual context threading because context is automatically available in any async operation; more efficient than request-scoped logging because context is stored once and accessed multiple times.
+4 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 atlas-mcp-server at 35/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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