ai-mldeveloper-tools

AgentRPC

agentrpc

by agentrpc

AgentRPC is a universal RPC layer for AI agents, enabling seamless connection to any function, language, or framework in

A universal RPC layer for AI agents. Connect to any function, any language, any framework, in minutes.

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Multi-language support (TypeScript, Go, Python, .NET)No open ports required for private networksBuilt-in observability and health monitoring

best for

  • / AI agents needing to call services in private VPCs
  • / Cross-language function integration for AI systems
  • / Distributed AI applications across multiple cloud environments
  • / Teams building AI agents with complex backend integrations

capabilities

  • / Connect functions across multiple programming languages
  • / Access services in private VPCs without open ports
  • / Execute long-running functions beyond HTTP timeouts
  • / Monitor function health with automatic failover
  • / Register functions with hosted RPC management
  • / Generate OpenAI-compatible tool definitions

what it does

AgentRPC provides a universal RPC layer that lets AI agents call functions across different languages and private networks. It wraps your functions and exposes them through MCP and OpenAI-compatible tool interfaces.

about

AgentRPC is an official MCP server published by agentrpc that provides AI assistants with tools and capabilities via the Model Context Protocol. AgentRPC is a universal RPC layer for AI agents, enabling seamless connection to any function, language, or framework in It is categorized under ai ml, developer tools.

how to install

You can install AgentRPC in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

Apache-2.0

AgentRPC is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

AgentRPC

NPM Version GitHub go.mod Go version PyPI - Python Version License

Universal RPC layer for AI agents across network boundaries and languages

Overview

AgentRPC allows you to connect to any function, in any language, across network boundaries. It's ideal when you have services deployed in:

  • Private VPCs
  • Kubernetes clusters
  • Multiple cloud environments

AgentRPC wraps your functions in a universal RPC interface, connecting them to a hosted RPC server accessible through open standards:

  • Model Context Protocol (MCP)
  • OpenAI-compatible tool definitions (OpenAI, Anthropic, LiteLLM, OpenRouter, etc.)
<p align="center"> <img src="./assets/deployment.png" alt="deployment" width="500"> </p>

How It Works

  1. Registration: Use our SDK to register functions and APIs in any language
  2. Management: The AgentRPC platform (api.agentrpc.com) registers the function and monitors its health
  3. Access: Receive OpenAPI SDK compatible tool definitions and a hosted MCP server for connecting to compatible agents

Key Features

FeatureDescription
Multi-language SupportConnect to tools in TypeScript, Go, Python and .NET (coming soon)
Private Network SupportRegister functions in private VPCs with no open ports required
Long-running FunctionsLong polling SDKs allow function calls beyond HTTP timeout limits
Full ObservabilityComprehensive tracing, metrics, and events for complete visibility
Automatic FailoverIntelligent health tracking with automatic failover and retries
Framework CompatibilityOut-of-the-box support for MCP and OpenAI SDK compatible agents

Getting Started

Quick Start

Follow the quick start example on our docs site.

Examples

Explore working examples in the examples directory.

MCP Server

The AgentRPC TypeScript SDK includes an optional MCP (Model Context Protocol) server.

ANGENTRPC_API_SECRET=YOUR_API_SECRET npx agentrpc mcp

This launches an MCP-compliant server for external AI models to interact with your registered tools.

Claude Desktop Integration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "agentrpc": {
      "command": "npx",
      "args": [
        "-y",
        "agentrpc",
        "mcp"
      ],
      "env": {
        "AGENTRPC_API_SECRET": "<YOUR_API_SECRET>"
      }
    }
  }
}

More Info

Cursor Integration

Add to your ~/.cursor/mcp.json:

{
  "mcpServers": {
    "agentrpc": {
      "command": "npx",
      "args": ["-y", "agentrpc", "mcp"],
      "env": {
        "AGENTRPC_API_SECRET": "<YOUR_API_SECRET>"
      }
    }
  }
}

More Info

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

This repository contains all the open-source components and SDKs for AgentRPC.

FAQ

What is the AgentRPC MCP server?
AgentRPC is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for AgentRPC?
This profile displays 39 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.639 reviews
  • Fatima Gonzalez· Dec 12, 2024

    We wired AgentRPC into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Hiroshi Menon· Dec 8, 2024

    Useful MCP listing: AgentRPC is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Sakshi Patil· Dec 4, 2024

    Strong directory entry: AgentRPC surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Sakura Khanna· Dec 4, 2024

    We evaluated AgentRPC against two servers with overlapping tools; this profile had the clearer scope statement.

  • Ama Haddad· Nov 27, 2024

    AgentRPC is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • William Bansal· Nov 23, 2024

    AgentRPC has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Kwame Wang· Nov 3, 2024

    AgentRPC reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • William Agarwal· Nov 3, 2024

    Strong directory entry: AgentRPC surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Ama Tandon· Oct 22, 2024

    Useful MCP listing: AgentRPC is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Carlos Robinson· Oct 18, 2024

    We wired AgentRPC into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

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