| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides intelligent code completion for Emacs Lisp by analyzing the current buffer context, function signatures, and variable bindings. Works by parsing the elisp AST to understand scope and available symbols, then filtering completion candidates based on semantic relevance rather than simple prefix matching. Integrates with Emacs' native completion UI to deliver suggestions inline.
Unique: Runs completion logic inside Emacs via MCP rather than as a separate language server, allowing direct access to Emacs' runtime symbol table and buffer state without serialization overhead
vs alternatives: Faster and more accurate than regex-based completion because it leverages Emacs' native symbol introspection and live function definitions rather than static analysis
Extracts function signatures, argument lists, and docstrings from elisp code by introspecting function objects at runtime or parsing function definitions statically. Returns structured metadata including parameter names, optional/rest arguments, and documentation, enabling IDE-like hover hints and signature help. Integrates with MCP to deliver this metadata to client tools.
Unique: Combines runtime introspection (via Emacs' function-documentation and help-function-arglist) with static AST parsing to handle both loaded and unloaded code, providing complete signature coverage
vs alternatives: More complete than static-only analysis because it accesses live function objects with their actual arity and docstrings, and more reliable than pure runtime introspection because it falls back to parsing for unloaded code
Provides MCP-based access to Emacs buffer and file operations, allowing external tools to read, write, and manipulate buffers and files within the Emacs session. Supports operations like opening files, creating buffers, reading buffer content, and saving changes. Integrates with Emacs' buffer management to ensure consistency.
Unique: Exposes Emacs' buffer and file operations through MCP, allowing external tools to interact with Emacs buffers as if they were local files, with full integration into Emacs' buffer management system
vs alternatives: More integrated than file-system-only approaches because it can access Emacs buffers that may not be saved to disk, and respects Emacs' buffer modes and encoding settings
Enables jumping to function and variable definitions by resolving symbols to their source locations in the Emacs codebase or loaded packages. Uses Emacs' native find-function and find-variable mechanisms combined with source file indexing to map symbols to file paths and line numbers. Exposes this via MCP to support IDE-style 'go to definition' workflows.
Unique: Leverages Emacs' built-in find-function and find-variable commands which have deep knowledge of the Emacs installation and package load paths, rather than implementing custom symbol resolution
vs alternatives: More reliable than generic language server approaches because it uses Emacs' native symbol resolution which understands autoload directives, package load order, and Emacs-specific conventions
Performs static analysis and runtime validation of elisp code to detect syntax errors, undefined variables, and common mistakes. Combines byte-compilation (via Emacs' native byte-compiler) with custom linting rules to catch issues like unused variables, incorrect function calls, and type mismatches. Reports diagnostics via MCP in LSP-compatible format for integration with editor linters.
Unique: Integrates Emacs' native byte-compiler as the primary validation engine, which understands elisp semantics deeply, combined with custom linting rules that catch Emacs-specific anti-patterns
vs alternatives: More accurate than generic linters because it uses the actual Emacs byte-compiler which understands elisp's dynamic nature, and more comprehensive than simple regex-based checkers because it performs semantic analysis
Supports automated refactoring operations like renaming functions and variables across multiple files, and extracting code into new functions. Works by analyzing the symbol table to find all references to a symbol, then applying transformations while respecting scope and shadowing rules. Uses buffer manipulation and file I/O to apply changes atomically.
Unique: Performs refactoring by analyzing Emacs' live symbol table and scope rules, ensuring that shadowed variables and local bindings are handled correctly, rather than using simple text-based search-and-replace
vs alternatives: More accurate than text-based refactoring tools because it understands elisp's scoping rules and can distinguish between different symbols with the same name in different scopes
Enables executing elisp code snippets directly within the Emacs session via MCP, with results returned to the client. Supports evaluating expressions, loading files, and inspecting the state of the running Emacs instance. Integrates with Emacs' eval function and provides access to the current environment (variables, functions, buffers).
Unique: Provides direct access to the running Emacs process via MCP, allowing evaluation in the actual environment where code will run, rather than simulating execution in a separate sandbox
vs alternatives: More powerful than static analysis because it can test code in the actual Emacs environment with all loaded packages and configurations, but requires careful handling of side effects
Analyzes elisp code to extract package dependencies, version requirements, and load-path configuration. Parses require and use-package declarations to build a dependency graph, then validates that all dependencies are available and compatible. Integrates with Emacs' package management system (package.el) to check installed versions.
Unique: Analyzes both static require/use-package declarations and queries the live Emacs package system to validate that dependencies are actually installed, combining static and runtime analysis
vs alternatives: More accurate than parsing Package-Requires headers alone because it also detects dynamic requires and validates against the actual installed packages in the Emacs session
+3 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 elisp-dev-mcp at 23/100.
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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