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"Integrating Automated Payment Systems" matching MCP servers:

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    An MCP server that allows users to run and visualize systems models using the lethain:systems library, including capabilities to run model specifications and load systems documentation into the context window.
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    MIT
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    LFWin Payment MCP Server is a payment capability service for AI Agents. Adhering to the standard MCP protocol, it exposes capabilities including creating payment orders, checking payment status, initiating refunds and querying refund status for Cursor, Cline, Claude Desktop, enterprise intelligent assistants and self-developed Agent applications.
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    A Model Context Protocol (MCP) server for integrating with the SATIM payment gateway system in Algeria, enabling AI assistants to process CIB/Edhahabia card payments through the SATIM-ePAY platform.
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    GPL 3.0
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    Enables blockchain payment operations across multiple networks including Base, Ethereum, BSC, Polygon, and Avalanche. Supports balance queries, token transfers, transaction tracking, wallet management, and gas fee estimation with built-in security limits.
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    MIT
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    Enables AI agents to interact with multiple payment providers (Stripe, Paystack) through a unified API. Supports payment initialization, verification, refunds, customer management, and invoicing without requiring knowledge of specific provider implementations.
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    MARM MCP provides persistent memory and structured session context beneath any AI tool, so your agents learn, remember, and collaborate across all your workflows.
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    MIT
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    MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.
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    Alpaca trading API with a hard daily USD cap enforced server-side. Paper trading by default — live trading blocked until you explicitly opt in.
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    Provides human-like memory dynamics for AI assistants where memories naturally fade over time unless reinforced through use, mimicking the Ebbinghaus forgetting curve. Enables automatic saving, searching, and management of contextual information with temporal decay algorithms.
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    AGPL 3.0
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    Content-addressed code graph that produces ranked context for AI agents in one call. 22 MCP tools across indexing, blast radius, test scope, semantic diff, runtime traffic, and feedback-aware context packing. Incremental updates via Merkle DAG (no re-indexing). GCF wire format saves 84% tokens vs JSON
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