# codemesh

> Use this tool when you need to automate and customize MCP server management with flexible TypeScript code, solving problems of manual server orchestration and limited documentation. CodeMesh enables AI agents to generate and improve tool documentation through auto-augmentation, streamlining server management workflows. It takes TypeScript code as input and outputs customized server orchestration, ideal for use cases requiring dynamic and adaptive server management.

Canonical page: https://skillsregistry.net/skills/kiliman-codemesh  
JSON: https://api.skillsregistry.net/v1/skills/kiliman-codemesh

## Description

CodeMesh enables AI agents to orchestrate any MCP server by writing TypeScript code, with self-improving capabilities through auto-augmentation of tool documentation.

## Trust

- **Trust score (0–1):** 0.67
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-01

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/bm7i66vtrf)
- **Repository:** <https://github.com/kiliman/codemesh>

## 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": "kiliman-codemesh"
    }
  }
}
```

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