| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol specification to expose read_notes and search_notes as discoverable tools to MCP clients like Claude Desktop. The server registers tool schemas with the MCP client during initialization, enabling standardized tool discovery and invocation through the MCP protocol's tool_call mechanism. This allows any MCP-compatible client to dynamically discover available capabilities without hardcoding tool knowledge.
Unique: Implements full MCP server specification with tool registry pattern, enabling clients to discover tools via MCP's list_tools and call_tool methods rather than requiring hardcoded tool knowledge or custom integrations
vs alternatives: More standardized and client-agnostic than REST API wrappers or custom integrations, allowing any MCP-compatible client to access Obsidian without modification
Reads markdown file contents from the vault by accepting relative file paths, validating them through a Path Validator component that prevents directory traversal attacks (../ patterns), symlink escapes, and hidden file access. The validator ensures all file operations remain within the configured vault root directory before passing validated paths to the file system layer. This security-first approach allows safe external access to vault contents while maintaining strict boundaries.
Unique: Implements multi-layer path validation (canonicalization, traversal pattern detection, symlink resolution, hidden file checks) as a dedicated Path Validator component rather than inline validation, enabling auditable and testable security boundaries
vs alternatives: More robust than simple string prefix matching because it canonicalizes paths and validates symlinks, preventing sophisticated traversal attacks that bypass naive string-based checks
Performs all file system operations asynchronously using Node.js async APIs (fs.promises) rather than blocking synchronous calls, preventing the server from hanging when reading large files or slow storage. Each operation includes comprehensive error handling for common failures (file not found, permission denied, encoding errors) with descriptive error messages returned to the client. This async-first approach ensures the server remains responsive even under load or with slow storage backends.
Unique: Uses Node.js fs.promises API for all file operations rather than mixing sync and async calls, ensuring consistent async behavior and preventing accidental blocking operations that could degrade server performance
vs alternatives: More responsive than synchronous file operations because it doesn't block the event loop, allowing the server to handle multiple concurrent requests without performance degradation
Searches the vault for markdown files matching glob patterns (e.g., '*.md', 'notes/**/project*.md') by accepting a search pattern, validating it through the Path Validator to prevent directory traversal in the pattern itself, then executing filesystem glob operations within the vault boundary. Returns a list of matching file paths relative to vault root. This enables AI assistants to discover relevant notes without requiring exact filenames.
Unique: Validates glob patterns through the same Path Validator component used for file reads, preventing attackers from crafting patterns that escape the vault directory, rather than treating search as a separate security domain
vs alternatives: Safer than exposing raw filesystem glob to untrusted clients because pattern validation prevents directory traversal within the search itself, whereas naive glob implementations could be exploited with patterns like '../../../etc/*'
Configures the MCP server to bind to a specific Obsidian vault directory or any markdown-containing directory through environment variables or configuration files. The server reads the vault path at startup, validates it exists and is readable, then uses it as the root boundary for all subsequent file operations. This configuration-driven approach allows a single mcp-obsidian installation to serve different vaults by changing configuration without code changes.
Unique: Uses environment variable-based configuration compatible with Smithery CLI and VS Code MCP extension installers, enabling one-click setup through standard MCP configuration patterns rather than requiring manual file editing
vs alternatives: More flexible than hardcoded vault paths because configuration can be changed per deployment, and more standardized than custom config formats because it follows MCP server configuration conventions
Retrieves markdown file contents exactly as stored, preserving all formatting, frontmatter (YAML/TOML), internal links, and metadata. The read_notes tool returns raw file content without parsing or transformation, allowing AI assistants to see the exact note structure including Obsidian-specific syntax like [[internal links]] and #tags. This preservation enables the AI to understand note relationships and metadata without requiring separate metadata extraction.
Unique: Returns raw markdown without parsing or normalization, preserving Obsidian-specific syntax like [[links]] and #tags as-is, allowing AI models to understand vault structure directly rather than requiring intermediate transformation layers
vs alternatives: More transparent than APIs that parse and normalize markdown because the AI sees exactly what's in the vault, enabling it to understand internal link graphs and metadata relationships without additional context
Enables understanding of Obsidian's internal link graph by exposing raw markdown content that contains [[wiki-style links]] and backlinks. While mcp-obsidian doesn't explicitly parse the link graph, it provides the raw markdown that contains all link information, allowing AI assistants to extract and reason about note relationships by analyzing the [[note-name]] syntax. Combined with the search capability, this enables graph traversal patterns where the AI can follow links to discover related notes.
Unique: Preserves Obsidian's [[wiki-link]] syntax in raw markdown rather than normalizing to standard markdown links, allowing AI to understand Obsidian-specific linking semantics and potentially distinguish between different link types or contexts
vs alternatives: More flexible than a pre-computed link graph API because the AI can apply custom logic to link discovery (e.g., filtering by link context, understanding link metadata), though it requires more client-side processing
Implements the MCP server specification in a way that's compatible with multiple MCP clients including Claude Desktop, VS Code with MCP extension, and custom MCP client implementations. The server exposes tools through standard MCP protocol methods (list_tools, call_tool) without client-specific code paths, allowing the same server instance to serve different clients simultaneously. This client-agnostic approach is enabled by strict adherence to the MCP specification.
Unique: Implements MCP specification without client-specific code paths or adaptations, ensuring true client-agnosticism through strict protocol compliance rather than special-casing for popular clients
vs alternatives: More portable than custom integrations because any MCP-compatible client can use it without modification, whereas REST APIs or custom protocols require client-specific implementations
+3 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.
Obsidian MCP Server scores higher at 60/100 vs Supabase at 42/100.
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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