| Ecosystem |
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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Translates Model Context Protocol (MCP) JSON-RPC requests from LLM clients into HTTP GET/POST calls targeting a local Binary Ninja plugin HTTP server on port 9009. Uses FastMCP framework to expose 64 reverse engineering tools as standardized MCP tool definitions, enabling seamless integration between LLM clients (Claude Desktop, Cline, Cursor, etc.) and Binary Ninja's analysis engine without requiring direct Binary Ninja API knowledge from the LLM.
Unique: Implements a three-tier architecture (LLM Client → MCP Bridge → HTTP Server → Binary Ninja Plugin) that decouples the MCP protocol layer from Binary Ninja's native API, allowing multiple MCP clients to connect to a single Binary Ninja instance without client-specific modifications. Uses FastMCP's tool registry pattern to dynamically expose Binary Ninja capabilities as standardized MCP tools.
vs alternatives: Provides native MCP support for Binary Ninja whereas alternatives require custom REST API wrappers or direct Binary Ninja Python API calls, making it the only standardized bridge for MCP-compatible LLM clients.
Exposes Binary Ninja's function analysis capabilities through HTTP endpoints that retrieve detailed metadata about functions in loaded binaries, including function names, type signatures, parameter types, return types, and internal control flow information. The BinaryOperations layer queries Binary Ninja's internal function objects and type system to construct structured JSON responses containing function-level analysis without requiring the LLM to understand Binary Ninja's Python API.
Unique: Leverages Binary Ninja's internal function objects and type inference engine to provide structured function metadata through HTTP endpoints, avoiding the need for LLMs to parse disassembly or understand calling conventions. The BinaryOperations layer abstracts Binary Ninja's Python API complexity into simple JSON responses.
vs alternatives: Provides richer function metadata than IDA Pro's REST API and requires no manual type annotation, as Binary Ninja's type inference is performed automatically during binary analysis.
Provides a plugin architecture that allows developers to extend the Binary Ninja MCP bridge with custom tools and analysis capabilities. Developers can register new HTTP endpoints in the BinaryNinjaEndpoints class and expose them as MCP tools through the bridge, enabling custom reverse engineering workflows without modifying the core bridge code. The architecture supports adding new tools by implementing simple HTTP endpoint handlers that follow the existing pattern.
Unique: Implements a simple plugin architecture where developers can register custom HTTP endpoints that are automatically exposed as MCP tools, without requiring knowledge of the MCP protocol. The BinaryNinjaEndpoints class acts as a registry that maps HTTP routes to Binary Ninja operations.
vs alternatives: Provides easier extensibility than building custom MCP servers from scratch because it abstracts the MCP protocol layer and provides a simple HTTP endpoint registration pattern.
Exposes Binary Ninja's cross-reference (xref) tracking system through HTTP endpoints that identify all locations where a function, variable, or memory address is referenced within a binary. Queries Binary Ninja's internal xref graph to return caller/callee relationships, data references, and control flow dependencies, enabling LLMs to understand data flow and function call chains without manual graph traversal.
Unique: Wraps Binary Ninja's internal xref graph in HTTP endpoints that return structured JSON, allowing LLMs to reason about function call chains and data dependencies without understanding Binary Ninja's graph query API. Supports bidirectional xref queries (callers and callees) through a single abstraction layer.
vs alternatives: Provides more accurate xref tracking than Ghidra's REST API because Binary Ninja's analysis engine is more aggressive in identifying indirect calls and data references through type-aware analysis.
Enables LLMs to suggest and apply function renames and type annotations to a loaded binary through HTTP POST endpoints that modify Binary Ninja's internal function objects. The BinaryOperations layer validates rename requests and applies changes to the binary's symbol table, allowing LLMs to improve binary readability by assigning meaningful names based on code analysis without requiring manual Binary Ninja UI interaction.
Unique: Implements bidirectional communication where LLMs can not only read function metadata but also write changes back to the binary through HTTP POST endpoints, creating an interactive feedback loop. Validates all rename requests against C identifier rules before applying to prevent corrupting the binary's symbol table.
vs alternatives: Unlike read-only reverse engineering tools, this capability enables LLMs to actively improve binary analysis quality through iterative renaming and annotation, creating a collaborative human-AI workflow.
Provides HTTP endpoints to inspect memory contents and data structures at specific addresses in a loaded binary, with type-aware interpretation using Binary Ninja's type system. Queries memory regions, interprets raw bytes according to inferred or user-defined types, and returns structured representations of data structures, enabling LLMs to understand data layout and contents without manual hex dump parsing.
Unique: Combines Binary Ninja's type system with memory inspection to provide type-aware data interpretation, automatically converting raw bytes to structured representations based on inferred types. Abstracts the complexity of manual type casting and struct layout calculation.
vs alternatives: Provides more intelligent data interpretation than raw hex dump tools because it leverages Binary Ninja's type inference to automatically structure untyped memory regions.
Exposes HTTP endpoints to retrieve disassembled code for functions or address ranges, returning instruction-level details including mnemonics, operands, and metadata. The BinaryOperations layer queries Binary Ninja's IL (Intermediate Language) and disassembly representations to provide both high-level and low-level code views, enabling LLMs to analyze instruction sequences and understand control flow without requiring manual disassembly parsing.
Unique: Provides multiple levels of code abstraction (LLIL, MLIL, HLIL) through a single HTTP endpoint, allowing LLMs to choose between low-level instruction details and high-level pseudocode representations. Includes IL metadata that captures Binary Ninja's semantic analysis of instructions.
vs alternatives: Offers richer code representations than IDA Pro's REST API by exposing multiple IL levels, enabling LLMs to reason about code at different abstraction levels without requiring separate disassembly tools.
Provides HTTP endpoints to load, unload, and manage multiple binary files within a single Binary Ninja instance, enabling LLMs to switch between binaries or analyze related binaries in a single session. The plugin maintains a registry of loaded binaries and routes requests to the appropriate binary context, allowing complex analysis workflows that involve multiple executable files or libraries.
Unique: Implements a binary registry pattern that allows multiple binaries to be loaded and managed within a single Binary Ninja instance, with automatic context switching based on HTTP request parameters. Enables complex multi-binary workflows without requiring separate Binary Ninja instances.
vs alternatives: Provides better multi-binary support than standalone Binary Ninja because it abstracts binary switching through HTTP endpoints, allowing LLMs to seamlessly analyze multiple files without UI interaction.
+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 Binary Ninja at 26/100.
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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