| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 5 decomposed | 5 decomposed |
| Times Matched | 0 | 0 |
Enables Claude and other MCP clients to request transaction signatures from Phantom Wallet through a standardized Model Context Protocol server interface. The server acts as a bridge between LLM agents and the Phantom wallet extension, handling serialization of transaction objects, routing signature requests to the wallet's browser extension via postMessage API, and returning signed transactions back to the client with cryptographic proof of authorization.
Unique: Implements MCP protocol as a native bridge to Phantom Wallet's browser extension, using postMessage API for secure cross-context communication rather than exposing wallet APIs directly to the LLM, maintaining hardware wallet security guarantees while enabling agent-driven transaction workflows
vs alternatives: Provides MCP-standard interface for wallet integration (enabling Claude native support) while maintaining Phantom's security model, unlike direct RPC approaches that would require private key exposure or custom client implementations
Manages the lifecycle of connections between the MCP server and Phantom Wallet, handling wallet discovery, connection establishment, account enumeration, and network switching. The server maintains state about which wallet is connected, which accounts are available, and the current Solana network (mainnet, devnet, testnet), exposing this state as queryable MCP resources that clients can poll or subscribe to for real-time updates.
Unique: Exposes wallet state as first-class MCP resources rather than imperative function calls, allowing clients to declaratively query and subscribe to connection state changes, with automatic event propagation from Phantom's wallet change listeners to MCP resource updates
vs alternatives: Provides reactive state management through MCP's resource model rather than polling, reducing latency and enabling real-time UI updates in Claude and other clients when wallet state changes
Exposes MCP tool definitions that allow clients to construct Solana instructions and transactions without direct blockchain interaction. The server provides schema-based tool definitions for common Solana operations (token transfers, program invocations, system instructions), validates instruction parameters against Solana's IDL specifications, and returns properly formatted instruction objects that can be batched into transactions for signing.
Unique: Implements instruction composition as schema-based MCP tools with automatic parameter validation against Solana IDL specifications, allowing Claude to generate valid instructions through natural language without understanding binary encoding, while maintaining type safety through JSON schema definitions
vs alternatives: Abstracts Solana's binary instruction format through MCP's schema-based tool interface, enabling non-expert developers to compose transactions through Claude's natural language, whereas direct Web3.js usage requires understanding Solana's low-level instruction encoding
Subscribes to Phantom Wallet's event stream (account changes, network switches, wallet disconnections) and relays these events to MCP clients through the MCP protocol's notification mechanism. The server maintains event listeners on the Phantom extension's postMessage API, buffers events, and pushes them to connected clients, enabling real-time awareness of wallet state changes without polling.
Unique: Bridges Phantom's browser extension event model to MCP's notification protocol, enabling server-to-client push notifications for wallet state changes rather than client polling, with automatic event buffering and delivery guarantees at the MCP layer
vs alternatives: Provides push-based event delivery through MCP notifications rather than requiring clients to poll wallet state, reducing latency and enabling reactive workflows that respond immediately to wallet changes
Coordinates signing of transactions that require multiple signers by managing signature collection, tracking which signers have approved, and assembling the final signed transaction once all required signatures are gathered. The server maintains a transaction signing session, routes signature requests to appropriate signers (via Phantom or other wallet providers), and combines signatures into a valid Solana transaction that can be submitted to the blockchain.
Unique: Implements transaction signing sessions with state tracking for multi-signer coordination, managing signature collection and assembly through MCP tool calls rather than requiring clients to manually orchestrate multiple wallet interactions, with automatic signer sequencing validation
vs alternatives: Abstracts multi-sig coordination complexity through MCP tools, enabling Claude to orchestrate multi-signer transactions through natural language, whereas manual approaches require clients to manage signature state and ordering themselves
ChatGPT utilizes a transformer-based architecture to generate responses based on the context of the conversation. It employs attention mechanisms to weigh the importance of different parts of the input text, allowing it to maintain context over multiple turns of dialogue. This enables it to provide coherent and contextually relevant responses that evolve as the conversation progresses.
Unique: ChatGPT's use of fine-tuning on conversational datasets allows it to better understand nuances in dialogue compared to other models that may not be specifically trained for conversation.
vs alternatives: More contextually aware than many rule-based chatbots, as it leverages deep learning for understanding and generating human-like dialogue.
ChatGPT employs a multi-layered neural network that analyzes user input to identify intent dynamically. It uses embeddings to represent user queries and matches them against a vast array of learned intents, enabling it to adapt responses based on the user's needs in real-time. This capability allows for more personalized and relevant interactions.
Unique: The model's ability to leverage contextual embeddings for intent recognition sets it apart from simpler keyword-based systems, allowing for a more nuanced understanding of user queries.
vs alternatives: More effective than traditional keyword matching systems, as it understands context and intent rather than relying solely on predefined keywords.
ChatGPT manages multi-turn dialogues by maintaining a conversation history that informs its responses. It uses a sliding window approach to keep track of recent exchanges, ensuring that the context remains relevant and coherent. This allows it to handle complex interactions where user queries may refer back to previous statements.
ChatGPT scores higher at 43/100 vs @phantom/mcp-server at 20/100. However, @phantom/mcp-server offers a free tier which may be better for getting started.
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Unique: The implementation of a dynamic context management system allows ChatGPT to effectively manage and reference prior interactions, unlike simpler models that may reset context after each response.
vs alternatives: Superior to basic chatbots that lack memory, as it can recall and reference previous messages to maintain a coherent conversation.
ChatGPT can summarize lengthy texts by analyzing the content and extracting key points while maintaining the original context. It utilizes attention mechanisms to focus on the most relevant parts of the text, allowing it to generate concise summaries that capture essential information without losing meaning.
Unique: ChatGPT's summarization capability is enhanced by its ability to maintain context through attention mechanisms, which allows it to produce more coherent and relevant summaries compared to simpler models.
vs alternatives: More effective than traditional summarization tools that rely on extractive methods, as it can generate summaries that are both concise and contextually accurate.
ChatGPT can modify its tone and style based on user preferences or contextual cues. It analyzes the input text to determine the desired tone and adjusts its responses accordingly, whether the user prefers formal, casual, or technical language. This capability enhances user engagement by tailoring interactions to individual preferences.
Unique: The ability to adapt tone and style dynamically based on user input distinguishes ChatGPT from static response systems that lack this level of personalization.
vs alternatives: More responsive than traditional chatbots that provide fixed responses, as it can tailor its language style to match user preferences.