# Tenderly

> Use this tool when you need to simulate and test blockchain transactions, or debug smart contract interactions, to identify and resolve issues without incurring gas costs. Tenderly provides a comprehensive infrastructure for blockchain development, offering features such as transaction simulation, private mainnet forks, and contract inspection. It is ideal for use cases where developers need to test, trace, and optimize blockchain applications, with 33 tools available for tasks like virtual test nets, network discovery, and project management.

Canonical page: https://skillsregistry.net/skills/tenderly  
JSON: https://api.skillsregistry.net/v1/skills/tenderly

## Description

Tenderly provides comprehensive blockchain development infrastructure through its MCP server. Simulate any transaction without spending gas, create private mainnet forks for testing, trace transaction execution with full call trees and state diffs, and inspect contract metadata including ABIs and token standards. The server exposes 33 tools covering Virtual TestNets, transaction simulation, tracing, contract inspection, network discovery, and project management.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** finance
- **Updated:** 2026-09-02

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/tenderly)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "tenderly"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/tenderly` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/tenderly/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt