# klanex-mcp

> klanex-mcp — chrassy-klanex-mcp. Use this tool when you need to reliably execute asynchronous agent tool calls with robust error handling and security features. It solves problems such as rate limits, outages, and hallucinated payloads by utilizing retries, circuit breakers, and idempotency, while providing inputs like encrypted credentials and outputs like signed-webhook results. It is ideal for use cases requiring high availability and self-correction capabilities, with interfaces that include human approval gates and failed call returns with llm_hint for agent self-correction.

Canonical page: https://skillsregistry.net/skills/chrassy-klanex-mcp  
JSON: https://api.skillsregistry.net/v1/skills/chrassy-klanex-mcp

## Description

Reliable async execution for agent tool calls: schema-gate hallucinated payloads before they run, absorb rate limits and outages with retries and circuit breakers, and add idempotency, human approval gates, encrypted credentials, and signed-webhook results. Failed calls return an llm_hint the agent can self-correct from.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** verified
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/gvo7mekwrc)
- **Repository:** <https://github.com/chrassy/klanex-mcp>

## 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": "chrassy-klanex-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/chrassy-klanex-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/chrassy-klanex-mcp/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