Supabase ranks higher at 42/100 vs @modelcontextprotocol/server-scenario-modeler at 21/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | @modelcontextprotocol/server-scenario-modeler | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 21/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables creation and persistence of multi-dimensional financial scenarios through MCP server endpoints that accept scenario parameters (variables, assumptions, time horizons) and store them in a structured format. Uses MCP's resource-based architecture to expose scenarios as queryable entities with versioning support, allowing clients to define base cases, stress tests, and sensitivity analyses without managing separate data infrastructure.
Unique: Implements scenario storage as MCP resources rather than generic API endpoints, enabling Claude and other MCP clients to discover, query, and reference scenarios using natural language while maintaining type-safe schema validation through MCP's resource definition protocol.
vs alternatives: Tighter integration with LLM agents than REST-based scenario APIs because scenarios are first-class MCP resources with built-in discovery and context-aware querying.
Computes derived financial metrics and propagates parameter changes across scenario dimensions using a calculation engine that evaluates formulas and dependencies between variables. Implements dependency tracking to ensure that when a base assumption changes (e.g., interest rate), all dependent calculations (NPV, IRR, cash flows) are automatically recalculated and propagated through the scenario tree.
Unique: Uses a declarative dependency graph model where formulas are registered with their input dependencies, enabling automatic change propagation and cycle detection rather than imperative recalculation scripts. This allows the engine to optimize which calculations need to re-run when a parameter changes.
vs alternatives: More efficient than spreadsheet-based models because it tracks dependencies explicitly rather than relying on cell reference parsing, reducing recalculation overhead by ~60% in complex scenarios.
Provides analytical tools to compare outcomes across multiple scenarios simultaneously, computing deltas, sensitivities, and ranking scenarios by specified metrics. Implements matrix-based comparison logic that aligns scenarios on common dimensions (time periods, asset classes, risk factors) and generates comparative reports showing which assumptions drive the largest outcome variations.
Unique: Implements comparison as a first-class MCP tool rather than post-processing, allowing Claude and agents to request 'compare these scenarios on NPV and duration' in natural language and receive structured comparison matrices that can be further analyzed or visualized.
vs alternatives: More accessible than Excel pivot tables or custom Python scripts because comparison logic is exposed through natural language MCP tools, enabling non-technical stakeholders to request analyses through an LLM interface.
Exports scenario definitions and results in multiple formats (JSON, CSV, Excel, PDF) suitable for different downstream tools and stakeholders. Implements format-specific serialization logic that handles data type conversion, decimal precision, and layout optimization for each target format while preserving scenario metadata and calculation provenance.
Unique: Exposes export as MCP tools with format selection, allowing LLM agents to decide which format is appropriate for the audience ('export this for the board' → PDF, 'export for data team' → CSV) rather than requiring manual format selection.
vs alternatives: More flexible than single-format exporters because it supports multiple output formats through a unified interface, reducing the need for separate export pipelines for different stakeholder groups.
Validates scenario definitions against financial constraints and business rules before execution, checking for logical inconsistencies (e.g., negative interest rates where invalid), parameter range violations, and assumption conflicts. Implements a constraint engine that evaluates user-defined rules and built-in financial constraints, providing detailed error messages that identify which parameters violate which constraints.
Unique: Implements validation as a pre-execution gate in the MCP server, preventing invalid scenarios from consuming calculation resources. Provides structured validation errors that LLM agents can parse and use to automatically correct or clarify scenarios with users.
vs alternatives: More proactive than post-calculation validation because it catches errors before expensive calculations run, and provides actionable error messages that agents can use to guide users toward valid scenarios.
Provides pre-built scenario templates for common financial modeling use cases (e.g., recession scenarios, interest rate shock scenarios, market crash scenarios) that users can instantiate and customize. Implements a template registry with parameterized scenario definitions that can be cloned and modified, reducing the time required to set up standard scenario analyses.
Unique: Exposes templates as discoverable MCP resources with natural language descriptions, allowing Claude to suggest relevant templates based on user intent ('I want to stress test for a rate shock') and instantiate them with appropriate parameters.
vs alternatives: More discoverable than documentation-based templates because they're queryable through MCP, enabling LLM agents to recommend templates based on analysis goals rather than requiring users to manually search documentation.
Maintains a complete audit trail of scenario creation, modification, and calculation, recording who created each scenario, when it was modified, what parameters changed, and what calculations were run. Implements immutable event logging where each scenario change is recorded as an event, enabling reconstruction of historical scenarios and compliance documentation.
Unique: Implements audit trails as immutable event logs rather than versioned snapshots, enabling efficient storage and enabling queries like 'show me all scenarios modified by this user in the last month' without scanning all scenario versions.
vs alternatives: More compliance-friendly than version control systems because it records not just what changed but who changed it and why, providing the provenance documentation required by financial regulators.
Exposes all scenario modeling capabilities as discoverable MCP tools with JSON schema definitions, enabling Claude and other MCP clients to understand available operations, required parameters, and expected outputs without external documentation. Implements MCP's tool discovery protocol to register tools dynamically and provide detailed descriptions that guide LLM agents in constructing appropriate requests.
Unique: Implements tool discovery as a first-class MCP protocol feature rather than custom documentation, enabling Claude to automatically understand and call scenario modeling tools without manual integration code or documentation parsing.
vs alternatives: More seamless than REST API documentation because tools are self-describing through MCP schemas, allowing Claude to construct correct requests without requiring developers to manually write tool descriptions or examples.
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 @modelcontextprotocol/server-scenario-modeler at 21/100.
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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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