mcpField
A
AgentTwin

AgentTwin

Local
agenttwin.siDeveloper Tools0Updated 4d ago

About AgentTwin

AgentTwin agent tools provides public skills and documentation for developers integrating with AgentTwin, a Node.js workflow testing product for applications and AI agents. Its resources cover workflow preparation, failure investigation and replay; the documentation MCP offers search and read access only.

Key features

  1. 01Prepare repository workflows with fixtures and assertions
  2. 02Investigate recorded failures and test repairs
  3. 03Replay runs and record regression baselines
  4. 04Access public integration guidance for coding agents

Use cases

  1. 01Set up a repository workflow for AgentTwin testing
  2. 02Investigate a failed run and verify an application repair
  3. 03Replay a captured run to check a source-only repair
  4. 04Look up AgentTwin API and sandbox documentation

MCP support

The public MCP server at `https://agenttwin.si/mcp/docs` exposes `search_docs` and `read_doc` for documentation access. It requires no credentials and does not provide customer access or execution.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"agenttwin-agenttwin": {
"command": "npx",
"args": [
"-y",
"agenttwin"
]
}
}
}
Run commandnpx -y agenttwin

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmagenttwin0.3.27

Tools

33

Saved tool list · updated 3d ago

This tool takes no parameters.

Details

From the project README

AgentTwin agent tools

Official public integration resources for AgentTwin agent testing. The product implementation repository is private; this repository contains only public agent instructions, skills and read-only documentation MCP configuration.

AgentTwin is Node.js workflow testing for prepared applications and AI agents. It runs those workflows against synthetic service state, injects supported failures and records operations, state changes and assertions for review. It is a developer integration testing tool, separate from unrelated research projects that use the same name.

Skills

  • agenttwin-prepare — prepare a real repository workflow with explicit invocation, synthetic fixtures, supported operations and correctness assertions.
  • agenttwin-investigate — investigate recorded failures and verify an application repair against unchanged checks.
  • agenttwin-replay — replay a captured run, verify a source-only repair against unchanged checks, and record the restored baseline as a reviewed regression.

Install with a compatible skills client:

npx skills add AgentTwin/agenttwin-agent-tools

Review the skills before installing. This does not install the CLI or run application code. The AgentTwin Node CLI is separate.

The public skill detail pages are agenttwin-prepare and agenttwin-investigate and agenttwin-replay. Directory rankings and security assessments are independent.

Agent configuration

AGENTS.md gives coding agents public integration guidance. Cursor users can opt into .cursor/rules/agenttwin.mdc. The portable Agent Plugins manifest is plugin.json, with the documentation server configured in mcp.json. Use your client's supported plugin installer; client support varies.

The public MCP at https://agenttwin.si/mcp/docs supplies search_docs and read_doc. No credentials are required. It has no customer access or execution. The official MCP Registry entry links to the owned developer page and endpoint.

Maintained references

Local workflow execution can access the host filesystem and network. Review code, fixtures and invocation before running. Static discovery is not a runnable test; no production replica, provider parity or automatic repair is promised.

The canonical maintained source for these public files lives in AgentTwin's website integration bundle. MIT licensed; see LICENSE.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.