Supabase ranks higher at 42/100 vs Castra – Strip orchestration rights from your LLMs at 26/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Castra – Strip orchestration rights from your LLMs | Supabase |
|---|---|---|
| Type | CLI Tool | MCP Server |
| UnfragileRank | 26/100 | 42/100 |
| Adoption | 0 |
| 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Intercepts and modifies LLM prompts to remove or restrict orchestration directives, function-calling permissions, and tool-use capabilities before they reach the model. Works by parsing incoming prompts, identifying orchestration-related instructions (tool invocation, workflow control, agent loops), and either stripping them entirely or replacing them with constrained versions that prevent unauthorized execution. Uses pattern matching and instruction rewriting to maintain semantic intent while removing dangerous orchestration primitives.
Unique: Specifically targets orchestration and tool-calling capabilities rather than general content filtering — uses instruction-level analysis to surgically remove function invocation, agent loops, and workflow control directives while preserving legitimate prompt semantics
vs alternatives: More granular than generic content filters (which block broad categories) and more focused than full jailbreak defenses, enabling teams to selectively disable orchestration while keeping other LLM capabilities intact
Provides a command-line interface for batch processing prompts through a transformation pipeline that validates, modifies, and logs changes to LLM instructions. Accepts prompts as input (via stdin, files, or API), applies orchestration stripping rules, validates the output against a policy schema, and returns sanitized prompts with detailed change logs. Implements a composable filter chain architecture where each stage (detection, stripping, validation, logging) can be independently configured or extended.
Unique: Implements a composable filter-chain architecture where orchestration stripping, validation, and logging are independent stages that can be reordered or extended — enables teams to build custom sanitization pipelines without modifying core code
vs alternatives: More flexible than monolithic content filters and more automation-friendly than manual prompt review, with explicit audit trails suitable for compliance-heavy industries
Analyzes prompts to identify and classify different types of orchestration directives (tool-calling, function invocation, agent loops, workflow control, multi-step planning). Uses pattern recognition and semantic analysis to detect both explicit orchestration instructions (e.g., 'call the weather API') and implicit ones (e.g., 'use available tools to solve this'). Classifies detected capabilities by type and severity, enabling fine-grained policy decisions about which to allow, restrict, or remove.
Unique: Focuses specifically on orchestration-layer capabilities rather than general content or toxicity — uses domain-specific pattern libraries tailored to tool-calling APIs, agent frameworks, and workflow orchestration systems
vs alternatives: More precise than generic prompt analyzers because it understands the specific semantics of orchestration directives (function schemas, tool invocation syntax, agent loop patterns) rather than treating them as generic text
Enforces user-defined policies that specify which orchestration capabilities are allowed, restricted, or forbidden in prompts. Policies are defined as configuration files (YAML/JSON) that map capability types to enforcement actions (allow, restrict, deny). During prompt processing, the system checks detected capabilities against the policy and either permits them, applies restrictions (e.g., rate limiting, approval gates), or blocks them entirely. Supports role-based policies where different users or contexts have different capability allowances.
Unique: Implements a declarative policy language specifically for orchestration capabilities rather than generic content policies — enables fine-grained control over tool-calling, function invocation, and agent behavior without requiring code changes
vs alternatives: More flexible than hard-coded capability restrictions and more maintainable than custom filtering logic, with explicit policy versioning and audit trails suitable for compliance documentation
Automatically rewrites prompts to add explicit constraints on orchestration capabilities, converting unrestricted orchestration requests into bounded versions. For example, converts 'use any available tools to solve this' into 'use only the following tools: [list] and make at most 3 function calls'. Uses template-based rewriting that preserves the original intent while adding safety boundaries. Supports custom rewrite rules that can be tailored to specific LLM models or use cases.
Unique: Focuses on adding explicit orchestration constraints rather than removing capabilities entirely — uses template-based rewriting that preserves intent while bounding resource usage and function call depth
vs alternatives: More permissive than outright capability stripping while still providing safety guarantees, enabling teams to use orchestration features with explicit resource and behavioral boundaries
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 Castra – Strip orchestration rights from your LLMs at 26/100. Castra – Strip orchestration rights from your LLMs leads on adoption and 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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