| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 4 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables full-text and semantic search across barnsworthburning.net's curated collection of bookmarks and snippets using MCP's standardized resource protocol. The server exposes search endpoints that query Nick Trombley's digital commonplace book, returning matched entries with metadata (source, topic tags, snippet context). Search queries are processed server-side against an indexed collection spanning design, software, art, architecture, craft, writing, and literature domains.
Unique: Exposes a hand-curated, thematically-organized commonplace book as an MCP resource, allowing LLM agents to access high-signal reference material without requiring the model to maintain or index the collection itself. The curator (Nick Trombley) provides editorial judgment on relevance and quality, reducing noise compared to generic web search.
vs alternatives: Provides higher-quality, editorially-vetted results than generic web search or RAG over unfiltered content, while requiring zero setup or indexing on the client side — the MCP server handles all data management.
Implements the Model Context Protocol specification to expose barnsworthburning.net's bookmark and snippet collection as queryable resources that MCP-compatible clients can discover and invoke. The server implements MCP's resource and tool interfaces, allowing clients to list available search capabilities and execute queries through standardized request/response patterns. This abstraction decouples the knowledge source from any specific LLM platform or application framework.
Unique: Implements MCP as a first-class integration pattern rather than wrapping a REST API, meaning the server is designed from the ground up to work within MCP's resource and tool model. This allows seamless composition with other MCP servers and native integration into MCP-aware LLM platforms.
vs alternatives: Avoids the impedance mismatch of REST-to-MCP adapters by implementing MCP natively, resulting in cleaner capability discovery and more efficient context passing compared to tools that bolt MCP on top of existing HTTP APIs.
Supports filtering search results by topic categories and knowledge domains (design, software, art, architecture, craft, writing, literature, etc.) that are pre-assigned to bookmarks and snippets in the commonplace book. The server likely indexes entries with topic tags or domain metadata, allowing clients to constrain searches to specific areas of interest. This enables more focused retrieval when the user has a particular domain in mind.
Unique: Leverages the curator's editorial domain taxonomy to enable structured filtering, rather than relying on generic keyword matching or learned embeddings. This ensures that domain boundaries reflect human judgment about knowledge organization.
vs alternatives: More precise than keyword-based filtering because it respects the curator's intentional categorization, avoiding false positives from polysemous terms (e.g., 'design' in software vs. graphic design contexts).
Returns not just matching entries but also surrounding context — including source attribution, snippet excerpts, and potentially related bookmarks or cross-references. The server preserves the curator's annotations and metadata for each entry, allowing clients to understand the source, relevance, and relationship of retrieved items. This enables richer integration into LLM reasoning by providing both the content and its provenance.
Unique: Treats the commonplace book as a knowledge graph where entries have rich metadata and relationships, rather than a flat document collection. The curator's annotations and cross-references are first-class data, not afterthoughts.
vs alternatives: Provides better source attribution and context than generic RAG systems that strip metadata, enabling more transparent and traceable reasoning in LLM agents.
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 barnsworthburning at 27/100.
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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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