| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Fetches articles and documentation from deepwiki.com via HTTP requests and converts HTML/structured content into LLM-optimized markdown format. The MCP server acts as a bridge between Claude/LLM clients and deepwiki's content API, handling URL resolution, content extraction, and markdown serialization to ensure the fetched content is directly consumable by language models without additional parsing steps.
Unique: Implements MCP protocol as a standardized bridge to deepwiki content, enabling seamless integration with Claude and other MCP-compatible LLM clients without custom API wrappers. Uses server-side HTML-to-markdown conversion to optimize for LLM token efficiency and context window usage.
vs alternatives: Provides native MCP integration for deepwiki access (vs. manual web scraping or REST API calls), reducing integration friction for Claude users and enabling real-time knowledge retrieval within agentic workflows.
Implements the Model Context Protocol (MCP) server specification, exposing deepwiki content fetching as a standardized tool/resource that MCP-compatible clients (Claude, custom agents) can discover and invoke. The server handles MCP message routing, tool schema definition, request/response serialization, and lifecycle management according to the MCP specification.
Unique: Implements full MCP server lifecycle including tool discovery, schema validation, and request routing, allowing Claude and other MCP clients to treat deepwiki as a first-class integrated tool rather than an external API dependency.
vs alternatives: Provides standardized MCP integration (vs. custom REST wrappers or direct HTTP clients), enabling Claude to discover and invoke deepwiki tools automatically without manual configuration.
Transforms deepwiki's HTML content into LLM-optimized markdown using a structured parsing and serialization pipeline. The transformation preserves semantic structure (headings, lists, code blocks, links) while removing noise (scripts, styles, tracking) and normalizing formatting for consistent markdown output that minimizes token usage and improves LLM comprehension.
Unique: Implements LLM-aware markdown conversion that prioritizes token efficiency and semantic clarity over visual fidelity, using selective element extraction and normalization to produce markdown optimized for language model consumption rather than human reading.
vs alternatives: Produces cleaner, more LLM-friendly markdown than generic HTML-to-markdown converters by removing navigation/boilerplate and normalizing structure specifically for AI context windows.
Resolves deepwiki article identifiers (titles, URLs, search terms) into canonical deepwiki.com URLs and fetches the corresponding content. The capability handles URL normalization, redirect following, and content discovery to ensure reliable article retrieval even if URLs are malformed or articles have been moved.
Unique: Implements transparent URL resolution and normalization for deepwiki, allowing callers to reference articles by title or partial URL while the server handles canonicalization and redirect following internally.
vs alternatives: Abstracts deepwiki's URL structure away from clients, enabling more natural article references (titles vs. URLs) and reducing brittleness to URL structure changes.
Defines and validates MCP tool schemas that describe the deepwiki content fetching capability to MCP clients. The schema specifies input parameters (article URL/title), output format (markdown), and tool metadata, enabling MCP clients to understand how to invoke the tool and validate requests before sending them to the server.
Unique: Implements MCP-compliant tool schema definition that enables Claude and other MCP clients to auto-discover and validate deepwiki tool invocations, reducing integration friction and preventing malformed requests.
vs alternatives: Provides structured tool interface definition (vs. unstructured API documentation), enabling MCP clients to validate requests and Claude to understand tool capabilities without manual configuration.
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-deepwiki at 21/100. mcp-deepwiki leads on ecosystem, while Supabase is stronger on quality.
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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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