| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Obsidian vault contents through MCP resources, allowing LLM clients to query and retrieve notes by path, title, or content patterns. Implements resource discovery via MCP's resource listing protocol, enabling clients to enumerate vault structure and fetch note metadata (creation date, modified date, tags, frontmatter) alongside raw markdown content. Works by mounting the local vault filesystem as MCP resources with standardized URIs (obsidian://vault/path/to/note.md), allowing stateless access without requiring Obsidian application to be running.
Unique: Implements MCP resource protocol for Obsidian vaults, treating the vault filesystem as a queryable resource layer rather than requiring Obsidian API integration or plugin development. Uses MCP's standardized resource URIs and metadata patterns to expose vault contents, enabling any MCP-compatible client to access notes without Obsidian application running.
vs alternatives: Lighter-weight than Obsidian plugins or REST API wrappers because it leverages MCP's native resource discovery and doesn't require Obsidian daemon; more portable than direct filesystem access because it abstracts vault structure through MCP's standard interface.
Streams large note content in chunks through MCP's resource interface, preventing context window overflow when notes exceed LLM token limits. Implements pagination or streaming at the MCP protocol level, allowing clients to request partial note content (e.g., first N lines, specific sections) rather than loading entire files. Handles markdown parsing to identify section boundaries (headings, code blocks) for intelligent chunking that respects document structure.
Unique: Implements structure-aware chunking at the MCP resource layer, parsing markdown to identify logical sections (headings, code blocks) and allowing clients to request partial content by section rather than full-file retrieval. Avoids token waste by enabling selective content access.
vs alternatives: More efficient than naive full-file retrieval because it respects document structure for chunking; more flexible than fixed-size token chunking because it preserves semantic boundaries.
Implements full-text search across all notes in the vault through MCP tools, supporting regex patterns, tag filters, and metadata filters (date range, note type). Search is executed locally against the vault filesystem without requiring Obsidian application, using efficient indexing or streaming search to handle large vaults. Returns ranked results with context snippets (surrounding text) and metadata, allowing LLM clients to identify relevant notes before fetching full content.
Unique: Exposes vault search as an MCP tool rather than requiring Obsidian UI or API, enabling programmatic search from any MCP client. Includes context snippets in results, allowing LLM agents to make informed decisions about which notes to fetch without reading full content.
vs alternatives: More accessible than Obsidian's native search because it works without the application running; more structured than grep-based search because it returns ranked results with metadata and snippets.
Parses markdown links and backlinks within notes to build a graph of note relationships, exposing this graph through MCP tools for traversal and analysis. Identifies wikilinks ([[ ]]), markdown links ([text](path)), and backlinks (notes that reference the current note) by parsing markdown syntax. Allows clients to traverse the knowledge graph (e.g., 'get all notes linked from this note', 'find all notes that reference this note') to build contextual understanding of how notes relate. Implements graph traversal algorithms (BFS, DFS) to find connected components or paths between notes.
Unique: Parses markdown link syntax to build a queryable knowledge graph, exposing graph traversal as MCP tools. Enables AI agents to understand vault structure and relationships without requiring Obsidian's graph view or plugin ecosystem.
vs alternatives: More programmatic than Obsidian's visual graph view because it exposes relationships as queryable data; more efficient than manual link following because it computes backlinks and paths algorithmically.
Parses YAML frontmatter from notes and exposes metadata as queryable fields through MCP, enabling filtering and aggregation by custom properties. Extracts frontmatter using YAML parsing (not regex), supporting nested structures, arrays, and typed values (strings, numbers, dates, booleans). Allows clients to query notes by metadata (e.g., 'find all notes with status=draft', 'get notes created in the last week', 'list all notes with a specific author'). Implements metadata indexing for efficient filtering without scanning all note content.
Unique: Exposes YAML frontmatter as queryable structured data through MCP, enabling metadata-based filtering and aggregation without requiring Obsidian plugins. Uses proper YAML parsing rather than regex, supporting complex nested structures.
vs alternatives: More flexible than Obsidian's native filtering because it supports arbitrary metadata fields; more reliable than regex-based extraction because it uses proper YAML parsing.
Provides MCP tools to create new notes in the vault with optional template support, allowing LLM clients to generate and persist new content. Implements template substitution (variable replacement, conditional sections) to support structured note creation (e.g., creating meeting notes with date, attendees, agenda template). Writes notes to the vault filesystem with proper markdown formatting, frontmatter, and directory structure. Handles file naming conventions and conflict resolution (e.g., appending timestamps to avoid overwrites).
Unique: Enables AI-driven note creation through MCP, allowing LLM clients to persist generated content directly to the vault. Supports template substitution for structured note generation, bridging AI output and Obsidian's knowledge management system.
vs alternatives: More direct than copy-pasting AI output because it writes directly to the vault; more flexible than Obsidian's native templates because it can be driven by LLM reasoning and context.
Computes and exposes vault-wide statistics through MCP tools, including note count, total word count, tag distribution, link density, and temporal metrics (creation/modification trends). Aggregates metadata across all notes to provide insights into vault structure and growth. Implements efficient scanning to avoid re-computing statistics on every request, with optional caching or lazy evaluation. Returns statistics as structured JSON for use in dashboards, reports, or LLM reasoning.
Unique: Exposes vault analytics through MCP tools, enabling programmatic access to vault metrics without requiring Obsidian plugins or external tools. Provides structured statistics for LLM reasoning about vault scale and content distribution.
vs alternatives: More accessible than Obsidian's built-in statistics because it works without the application running; more programmatic than manual analysis because it aggregates metrics automatically.
Implements the Model Context Protocol (MCP) resource specification to expose Obsidian vault as a standardized resource layer, enabling any MCP-compatible client to access vault contents without custom integration code. Defines resource URIs (obsidian://vault/path/to/note.md), resource types (text/markdown, application/json for metadata), and MIME types for proper content negotiation. Handles MCP resource discovery (listing available resources), resource reading (fetching content), and resource metadata (size, modified time, content type). Implements MCP's resource subscription mechanism for real-time updates if vault changes.
Unique: Implements MCP resource protocol as the primary integration mechanism, treating the vault as a standardized resource layer rather than a custom API. Enables vault exposure to any MCP-compatible client without requiring client-specific code.
vs alternatives: More portable than custom REST APIs because it uses the standard MCP protocol; more discoverable than filesystem access because it implements MCP's resource discovery and metadata mechanisms.
+1 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 mcp-obsidian at 30/100.
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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