Supabase ranks higher at 42/100 vs Agentseed – Generate Agents.md from a Codebase at 31/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Agentseed – Generate Agents.md from a Codebase | Supabase |
|---|---|---|
| Type | Framework | MCP Server |
| UnfragileRank | 31/100 | 42/100 |
| Adoption | 0 |
| 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Parses source code files using Abstract Syntax Tree (AST) analysis to extract structural information including function signatures, class definitions, module exports, and dependency relationships. The tool traverses the AST to build a semantic map of the codebase architecture rather than relying on regex or simple text parsing, enabling accurate identification of public APIs and internal structure.
Unique: Uses language-specific AST parsers to build semantic codebase maps rather than simple text scanning, enabling accurate extraction of public APIs and structural relationships that can be reliably consumed by AI agents
vs alternatives: More accurate than regex-based code scanning because it understands actual code structure; more focused than full IDE indexing because it specifically targets agent-consumable API documentation
Transforms extracted codebase structure (functions, classes, exports, signatures) into a machine-readable Markdown document formatted specifically for AI agent consumption. The generator creates a structured Agents.md file that lists available functions, their parameters, return types, and usage context in a format optimized for LLM context windows and function-calling patterns.
Unique: Generates Agents.md specifically formatted for AI agent consumption rather than human-readable documentation, with emphasis on function signatures, parameters, and return types in a format optimized for LLM context windows
vs alternatives: More targeted than generic documentation generators because it focuses on agent-consumable API surface; more maintainable than manual Agents.md because it auto-updates from source code
Provides pluggable AST parser support for multiple programming languages, allowing the tool to analyze codebases written in JavaScript, TypeScript, Python, and potentially other languages. Each language uses its native or optimized parser (e.g., tree-sitter, Babel for JS/TS; ast module for Python) to ensure accurate structural extraction across heterogeneous codebases.
Unique: Abstracts language-specific parsing behind a unified interface, allowing single-pass analysis of heterogeneous codebases without separate tools per language
vs alternatives: More flexible than language-specific documentation tools because it handles multiple languages in one pass; more maintainable than custom regex patterns because it uses native language parsers
Monitors codebase changes (file additions, deletions, modifications) and regenerates only affected sections of Agents.md rather than re-parsing the entire codebase. Uses file hashing or modification timestamps to detect changes, then re-parses only modified files and updates the corresponding entries in the generated documentation.
Unique: Implements incremental parsing and selective Agents.md updates rather than full regeneration, enabling fast CI/CD integration and real-time documentation sync during development
vs alternatives: Faster than full re-parse on every change because it only processes modified files; more practical for CI/CD than manual documentation updates because it's automated and efficient
Extracts detailed function signatures including parameter names, types, default values, and return types from parsed code. For typed languages (TypeScript, Python with type hints), captures full type information; for untyped languages, infers or documents parameter positions. Generates structured metadata that includes arity, parameter order, and type constraints.
Unique: Extracts and preserves type information from source code to generate agent-consumable function signatures that include parameter types and constraints, not just names
vs alternatives: More precise than simple function name extraction because it includes type information; more reliable than runtime introspection because it works statically without executing code
Analyzes import statements and require() calls to build a dependency graph showing which modules depend on which others. Tracks both internal dependencies (within the codebase) and external dependencies (third-party libraries). Generates metadata about module relationships that helps agents understand code organization and potential side effects.
Unique: Builds a static dependency graph from import analysis rather than runtime introspection, enabling agents to understand code organization without executing code
vs alternatives: More comprehensive than simple import listing because it shows relationships between modules; more reliable than runtime analysis because it doesn't require code execution
Parses docstrings, JSDoc comments, and inline comments from source code and includes them in the generated Agents.md. Extracts structured documentation (parameter descriptions, return value docs, examples) from comment blocks and associates them with the corresponding functions. Handles multiple comment formats (JSDoc, Python docstrings, Markdown comments).
Unique: Automatically extracts and includes existing docstrings and comments in Agents.md rather than requiring separate documentation, keeping docs in sync with code comments
vs alternatives: More maintainable than separate documentation because it sources from code comments; more complete than code-only extraction because it includes human-written context and examples
Identifies which functions, classes, and variables are exported from modules (public API) versus internal/private. Analyzes export statements, visibility modifiers (private, protected, public), and naming conventions to determine what should be exposed to agents. Filters out internal implementation details and focuses on the public contract.
Unique: Filters codebase analysis to expose only public APIs rather than all functions, enabling agents to interact with stable contracts without seeing internal implementation
vs alternatives: More focused than full codebase documentation because it excludes internal details; more maintainable than manual API lists because it's derived from actual export statements
+2 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.
Supabase scores higher at 42/100 vs Agentseed – Generate Agents.md from a Codebase at 31/100.
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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