| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes JSON Resume documents through the Model Context Protocol, enabling LLM clients to read, validate, and transform resume data against the official JSON Resume schema. The MCP server acts as a bridge between unstructured resume content and structured schema-compliant formats, using schema validation to ensure data integrity before exposure to language models.
Unique: Implements MCP as a standardized protocol layer for resume data access, allowing any MCP-compatible LLM client (Claude, custom agents) to interact with resume documents through a schema-aware interface rather than direct file I/O or custom APIs
vs alternatives: Provides protocol-agnostic resume access (MCP) versus proprietary REST APIs or file-based approaches, enabling seamless integration with Claude and other MCP-native LLM clients without custom authentication or endpoint management
Implements the MCP resource protocol to expose resume documents as queryable resources with URI-based addressing (e.g., resume://user-id/resume.json). The server maintains a resource registry and handles MCP read/list operations, allowing LLM clients to discover and fetch resume data through standard MCP resource semantics without direct filesystem access.
Unique: Uses MCP's resource protocol (list/read operations) to abstract resume storage, enabling LLM clients to interact with resumes as discoverable, addressable resources rather than opaque file paths or database queries
vs alternatives: Cleaner than REST API wrappers for LLM integration because MCP resources are natively understood by Claude and other MCP clients, eliminating the need for custom function definitions or schema documentation
Exposes resume operations as MCP tools (callable functions) that LLM clients can invoke, such as 'analyze-resume', 'generate-summary', or 'extract-skills'. The server implements tool schemas with input validation and returns structured results, allowing LLMs to programmatically trigger resume processing workflows without direct code execution or external API calls.
Unique: Implements MCP tool protocol to expose resume operations as first-class LLM-callable functions with schema validation, enabling Claude and other MCP clients to chain resume analysis steps without context switching or custom API integration
vs alternatives: More composable than monolithic resume APIs because each operation is a discrete MCP tool that LLMs can combine in agentic workflows; avoids the latency and complexity of round-tripping through external REST endpoints
Validates resume documents against the JSON Resume schema specification, checking field types, required properties, and format constraints. The server returns detailed validation errors with field paths and remediation suggestions, enabling LLM clients to identify and fix schema violations before processing or storage.
Unique: Integrates JSON Schema validation directly into the MCP server, providing LLM clients with real-time schema compliance feedback without requiring separate validation services or external schema registries
vs alternatives: Tighter integration than client-side validation libraries because validation happens server-side with full context, enabling LLMs to request re-validation after modifications without re-parsing or re-uploading resume data
Transforms resume data from various input formats (plain text, CSV, unstructured JSON) into standardized JSON Resume format through parsing and field mapping. The server applies normalization rules (e.g., date standardization, skill deduplication) and returns schema-compliant output, enabling LLM clients to work with consistently formatted resume data.
Unique: Implements format-agnostic resume parsing with LLM-friendly error reporting, allowing MCP clients to request conversion with fallback to LLM interpretation for ambiguous fields rather than failing silently
vs alternatives: More flexible than rigid regex-based parsers because it can leverage LLM context to disambiguate field mappings; more reliable than pure LLM parsing because it validates output against JSON Resume schema
Extracts structured metadata from resume documents (e.g., candidate name, email, phone, job titles, skills, years of experience) and maintains an index for fast retrieval and filtering. The server exposes metadata as queryable fields, enabling LLM clients to search or filter resumes by criteria without parsing full documents.
Unique: Maintains a structured metadata index alongside full resume documents, enabling LLM clients to perform fast metadata queries without parsing full JSON Resume objects, reducing latency for filtering and search operations
vs alternatives: Faster than full-document parsing for filtering because metadata is pre-extracted and indexed; more flexible than database queries because LLM clients can dynamically compose filter criteria through MCP tool invocations
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 @mcpflow.io/mcp at 22/100. @mcpflow.io/mcp 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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