"Proof-of-concept examples for Multi-Component Processes (MCPs)" matching MCP servers:

Tamper-proof audit trail for AI decisions. 6 tools to record, verify, and export cryptographic proof chains via MCP.

An MCP server implementing a multi-phase backend for structured model interactions, bounded compilation, exact-hash confirmation, and Codex-run provenance, with trust-separation hardening.

Provides cryptographic governance receipts for AI agents, enabling pre-execution evaluation and signed verdicts (EXECUTE/BLOCK/REVIEW/SHADOW) with offline-verifiable audit trails.

Lets AI agents run verification policies, inspect structured proof bundles, and submit high-trust proof-of-work results to Gibwork through MCP tools. Works additively alongside existing Gibwork MCP setups, with optional policy, budget, and audit layers via a gateway.

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

An MCP server that exposes web lookup, save note, and list notes tools for a router-research-writer agent pipeline, enabling coordinated multi-agent workflows via the Model Context Protocol.

Facilitates structured multi-agent debates with arguments, rebuttals, and judgments across multiple rounds, enabling diverse AI personas to engage in formal debate and collaborative problem-solving.

Enables AI clients to perform autonomous multi-agent deep research through MCP tools, including deep research, quick search, and retrieval of archived Markdown reports with live web search and source citations.

An MCP server that exposes tools for sub-agent style reasoning across multiple LLM providers, enabling delegation of prompts to various models and running critique loops, debates, red-teaming, and answer ranking.

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.

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.

Orchestrates multiple AI agents (Product Manager, Software Architect, Engineer, QA, Reviewer) to collaboratively plan, design, implement, review, and improve software development projects via MCP tools.

Enables multiple CLI-based AI agents to collaborate as a coordinated team through shared task queues, shared memory, and a message bus, with DAG orchestration, rate-limit avoidance, parallel dispatching, and long-task management.

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

An AI-centric MCP server that enables automated Xilinx Vivado workflows, including project management, synthesis, implementation, and timing analysis. It allows AI agents to drive hardware design processes while integrating directly with the official Vivado GUI for visual context.

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 reproducible evaluation of AI agents on multi-source evidence investigation tasks, providing Docker-based scenarios, MCP tools, and automated scoring for payout reconciliation, performance-claim verification, and incident reconstruction.

Transforms your IDE into an autonomous multi-agent coding assistant that orchestrates complex codebase refactoring using LangGraph and LiteLLM.

Lets Claude Code instances discover each other and exchange messages instantly across projects, with persistent peer IDs and automatic message rotation.