"Bank of America" matching MCP servers:

Enables image generation using Gemini native models, supporting both single prompts and batch processing via a file-based queue. It allows for detailed configuration of aspect ratios and models using YAML frontmatter across various MCP-enabled clients.

A bank account for AI agents. Five accounts. Earn, borrow, invest, exchange — autonomously.

An MCP server adapter that allows LLM agents to autonomously play World of ClaudeCraft by wrapping the game's headless environment as MCP tools for state reading and action execution.

Structured reasoning MCP server that decomposes problems into atomic steps (premise, reasoning, hypothesis, verification, conclusion) with confidence scoring, live visualization, and approval feedback.

An AI-powered treasury management MCP server that enables users to perform commercial banking analysis, including cash flow forecasting, idle cash detection, and debt covenant monitoring. It provides tools to surface financial insights and optimize working capital through automated processing of bank and transaction data.

A multi-agent orchestration system that enables multiple Claude instances to collaborate through a centralized hub with a shared workspace and real-time communication. It features integrated task management, role assignment, and persistent memory to facilitate complex, synchronized agent workflows.

Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.

Implements the Tree of Thoughts framework for structured reasoning and decision tree exploration, enabling LLMs to explore multiple reasoning paths, evaluate them, and backtrack.

Enables AI agents to manage self-custody Polygon wallets, transfer USDC, swap tokens, lend and borrow with collateral or reputation, post and complete jobs, negotiate prices, check balances, and earn yield.

AoT MCP server enables AI models to solve complex reasoning problems by decomposing them into independent, reusable atomic units of thought, featuring a powerful decomposition-contraction mechanism that allows for deep exploration of problem spaces while maintaining high confidence in conclusions.

Implements the Chain of Draft reasoning approach to generate minimalistic intermediate reasoning outputs while solving tasks, significantly reducing token usage while maintaining accuracy.

An MCP server that uses Groq's API to expose raw chain-of-thought tokens from Qwen's qwq model, enabling LLMs to think step-by-step before responding.

Enables AI assistants to control Discord servers with 93 tools for server management, moderation, voice TTS, AI chat via OpenRouter, and World of Warships stats lookup through the Model Context Protocol.

Enables LLM agents to make fast semantic decisions by choosing one of 2–16 options with probabilities, via tools for single and batch classification.

Enables graph analysis and network-flow computations through the Model Context Protocol, including shortest-path search, topological dependency resolution, maximum flow, minimum spanning trees, and PageRank centrality scoring for interconnected agent networks.

Enables users to run exposure audits, promotion gates, bank-health checks, counterexample hunts, and other verifier-backed tools on disposable data, returning inspectable certificates, SHA-256 receipts, and downloadable artifacts.

Transforms prompts into Chain of Draft (CoD) or Chain of Thought (CoT) format to enhance LLM reasoning quality while reducing token usage by up to 92.4%, supporting multiple LLM providers including Claude, GPT, Ollama, and local models.

Enables automated agents and writers to check Norwegian and English prose before sending, returning concrete style findings, revision hints, and verdicts instead of authorship claims, with a no-judgment band, positive quality checks, a private local mode, and phrase-bank memory.

Enables context capture and reinforcement learning by recording successful work patterns and creating reasoning chains for cross-conversation continuity. Automatically captures positive feedback through Claude Code hooks to build reusable success patterns.

MCP server providing shared working memory for collaborative AI agents, with real-time notes and LLM-consolidated structured memory bank.