| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes IDA Pro's native binary analysis engine through the Model Context Protocol, allowing Claude and other LLM clients to query disassembly, control flow graphs, function metadata, and cross-references without direct IDA GUI interaction. Uses MCP's JSON-RPC transport layer to serialize IDA's C++ analysis results into structured data that LLMs can reason about and act upon.
Unique: Bridges IDA Pro's proprietary C++ analysis engine to LLMs via MCP protocol, enabling Claude to directly query and reason about disassembly without requiring developers to write custom IDA Python plugins or REST wrappers
vs alternatives: Provides deeper binary analysis context than generic disassemblers (Ghidra, Radare2) by leveraging IDA's superior type inference and cross-reference tracking, while standardizing access through MCP instead of proprietary APIs
Retrieves the complete disassembly of a function by address or name, including operand resolution, cross-references, and metadata like function boundaries, calling conventions, and stack frame information. Implements IDA's internal function analysis to reconstruct human-readable assembly with symbolic references resolved.
Unique: Leverages IDA's internal function boundary detection and type inference to return semantically complete function disassembly with resolved operands, rather than raw instruction dumps
vs alternatives: More accurate than Ghidra's decompiler for complex calling conventions and indirect references because IDA's heuristics are more mature; faster than manual Radare2 scripting
Queries IDA's cross-reference database to build call graphs, data flow paths, and dependency chains between functions and data structures. Traverses xref edges (code-to-code, code-to-data, data-to-data) to identify relationships and propagate analysis context through the binary.
Unique: Exposes IDA's internal xref database as queryable graph structures, allowing LLMs to perform multi-hop reasoning across call chains without requiring manual graph construction
vs alternatives: More complete than static analysis tools like Cflow because IDA's xref tracking includes data references and indirect calls; faster than dynamic tracing for large binaries
Retrieves IDA's Hex-Rays decompiler output (pseudocode) for a function, translating low-level assembly into higher-level C-like code with variable recovery, type inference, and control flow reconstruction. Integrates with IDA's decompiler plugin to produce human-readable source approximations.
Unique: Integrates Hex-Rays decompiler output directly into MCP, allowing LLMs to reason about high-level pseudocode rather than assembly, with type recovery and variable tracking
vs alternatives: Hex-Rays decompilation is industry-leading for accuracy; Ghidra's decompiler is free but produces lower-quality output for complex code
Extracts structured metadata from the binary including segment layout, section information, entry points, imports, exports, and relocation tables. Parses PE/ELF/Mach-O headers through IDA's analysis to provide a complete binary blueprint for analysis planning.
Unique: Aggregates IDA's parsed binary headers and analysis into structured metadata, providing a single source of truth for binary layout without manual header parsing
vs alternatives: More complete than tools like readelf/objdump because IDA's analysis resolves symbolic references and handles multiple binary formats uniformly
Scans the binary for embedded strings, numeric constants, and data references, mapping them to their locations and associated functions. Uses IDA's string analysis to identify hardcoded values, error messages, and configuration data that may indicate functionality or vulnerabilities.
Unique: Leverages IDA's built-in string scanner to identify and contextualize embedded strings with function references, enabling LLMs to use strings as semantic anchors for code understanding
vs alternatives: More accurate than naive regex scanning because IDA's string detection handles encoding, alignment, and false positives; faster than manual binary grepping
Queries IDA's type inference engine to recover data structure layouts, function signatures, and variable types from binary analysis. Reconstructs struct definitions, union layouts, and function prototypes based on memory access patterns and calling convention analysis.
Unique: Exposes IDA's type inference engine to MCP clients, allowing LLMs to reason about recovered types and structures without manual reverse engineering
vs alternatives: IDA's type inference is more mature than Ghidra's for complex calling conventions; Radare2 lacks equivalent type recovery capabilities
Provides detailed analysis of individual instructions including operand types, memory access patterns, register usage, and semantic meaning. Interprets instruction sequences to identify common patterns (prologue/epilogue, loops, conditionals) and extract control flow semantics.
Unique: Provides instruction-level semantic analysis through IDA's processor modules, enabling LLMs to reason about low-level code behavior without requiring manual ISA knowledge
vs alternatives: More accurate than generic disassemblers because IDA's processor modules understand architecture-specific semantics; Capstone provides similar disassembly but lacks semantic context
+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 IDA Pro MCP at 22/100.
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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