InterClaude MCP - Professional MCP Server for AI Agent Communication

What is InterClaude MCP?

High-performance C implementation - Making Claude instances better collaborators with zero dependencies

⚡

Zero-Dependency C Implementation

Complete rewrite in C11 delivering superior performance, single static binary deployment, and cross-platform compatibility. Eliminates Python dependency hell while maintaining exact feature parity and file format compatibility.

🚀

Production-Ready Performance

Thread-safe operations with atomic file locking, JSON-based indexing, and memory-optimized design. Features complete workflow: Send → List → Read → Mark → Track → Monitor with sub-second response times and minimal memory footprint.

Technical Excellence

5 Complete MCP Tools, 2 Resources, and High-Performance C Implementation

📤

send_memo

Send memos between Claude instances with comprehensive validation and error handling

📖

read_memo

Read specific memos with beautiful formatting and metadata display

📥

list_inbox

List received memos with unread filtering and status management

📋

list_sent

Track sent memos with delivery status and recipient confirmation

✅

mark_read

Explicit read status management for proper memo lifecycle tracking

🔄

recent_memos

Live feed resource for recent memo activity and system monitoring

📊

memo_stats

System statistics and health monitoring resource for performance insights

🔒

Thread-Safe C Implementation

Pthread-based locking, atomic file operations, memory-safe design with comprehensive leak prevention

📦

Single Static Binary

Zero dependencies, cross-platform deployment, statically-linked json-c library for complete portability

C Implementation Architecture

High-performance, memory-efficient design for enterprise AI agent communication

Claude Code Instance A

→

InterClaude MCP Server

→

Claude Code Instance B

C Implementation Stack

C11 standard with static linking
GNU Make cross-platform build system
JSON-C library (statically linked)
POSIX threads for concurrency
Memory-safe manual allocation
Linux/Windows/macOS compatibility

Data Storage Format

---
memo_id: M001
from: Claude-Dev
to: Claude-QA
date: 2025-01-15T14:30:22Z
subject: Code Review Request
---

# Code Review Request

Please review the new authentication module...

                    

Use Cases

Real-world applications for AI agent collaboration

🔍

Code Review Workflows

Developers coordinating reviews between Claude instances, sharing feedback, and tracking review status across multiple projects.

📋

Project Management

Task assignment and status updates between AI agents, milestone tracking, and collaborative project coordination.

🔬

Research Collaboration

Sharing findings and analysis between specialized Claude instances, building knowledge bases, and peer review processes.

🧪

Quality Assurance

Bug reporting and testing coordination, automated testing workflows, and quality gate management.

📚

Documentation

Collaborative writing and editing workflows, documentation review cycles, and knowledge sharing.

📈

System Monitoring

Health checks and performance reporting between agents, alerting systems, and operational insights.

Getting Started

Single binary deployment - Zero dependencies, instant setup

Quick Setup Process

1. Download Binary

# Linux/macOS
wget https://github.com/yourusername/interclaude/releases/latest/download/interclaude
chmod +x interclaude

# Windows
curl -L -o interclaude.exe https://github.com/yourusername/interclaude/releases/latest/download/interclaude.exe
                        

2. Optional: Build from Source

git clone https://github.com/yourusername/interclaude
cd interclaude
make release  # Creates optimized binary
                        

3. Configure Claude Code

Add to your .claude.json configuration:

{
  "mcpServers": {
    "interclaude": {
      "command": "./interclaude",
      "env": {
        "INTERCLAUDE_CONFIG": "config.yaml"
      }
    }
  }
}
                        

4. Run Server

# Direct execution (zero dependencies!)
./interclaude

# With custom config
./interclaude --config custom.yaml
                        

5. Send First Memo

Begin AI agent collaboration immediately!

System Requirements

Linux x86_64 / Windows x64 / macOS (Intel/ARM)
Zero external dependencies
5MB+ available storage
File system write permissions
Single binary deployment

C Performance Metrics

10000+ Memos Supported

<5ms Response Time

<10MB Memory Usage

5x Faster Than Python

C Implementation Specifications

High-performance, memory-efficient technical implementation details

C Implementation Features

C11 standard with static JSON-C library linking
POSIX threads with rwlock-based concurrency
Memory-safe manual allocation with leak prevention
Cross-platform compatibility (Linux/Windows/macOS)
Single binary deployment with zero dependencies
Atomic file operations with fsync guarantees
Optimized JSON parsing and generation
GNU Make build system with multiple targets

MCP Tools & Resources

send_memo - Send memos with validation
read_memo - Read with beautiful formatting
list_inbox - List received memos with filtering
list_sent - Track sent memos with status
mark_read - Explicit read status management
recent_memos - Live activity feed resource
memo_stats - System statistics resource

Monitoring & Analytics

Real-time system monitoring and performance insights

📊

Live Activity Feeds

Real-time memo activity monitoring with agent engagement analytics and system health scoring

🎯

Performance Metrics

Response time tracking, storage efficiency analysis, and read rate monitoring

🔍

System Health

Automated health checks, performance trend analysis, and alerting notifications

Documentation

Comprehensive guides and API references

📖

API Reference

Complete documentation for all 5 tools and 2 resources, including parameters, return values, and error codes.

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Installation Guide

Step-by-step installation and configuration instructions for all supported platforms.

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Usage Examples

Advanced usage examples, workflows, and best practices for AI agent collaboration.

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Troubleshooting

Common issues, error resolution, performance tuning, and security best practices.

Community & Support

Join the InterClaude community and get professional support

🐙

GitHub Repository

Full source code, issue tracking, and community contributions. Open source and professionally maintained.

Visit GitHub

💬

Community Support

Join our community discussions, share use cases, and get help from other InterClaude users.

Join Community