rar-agent-finder

Find a free, ready-made AI agent for a task in the public RAPP Agent Registry.

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品質與安全性

A
說明品質
96%
結構描述完整度
96%
命名品質
88%
汙染風險
100%
權限相符程度
100%
協定合規性
100%

根據工具定義與協定合規性的自動化分析。

上下文成本

~693Token(工具定義)
~612 B典型回應大小
中等的注意力影響(128k 上下文的 0.54%)

這是每次將伺服器的工具載入模型上下文時所消耗的約略 token 數量。數量越高,可用於其他工作的注意力就越少。

安裝

一鍵安裝

將以下內容加入你的 `claude_desktop_config.json` 檔案:

{
  "mcpServers": {
    "rar-agent-finder": {
      "url": "https://rapp-agent-builder.azurewebsites.net/finder/mcp"
    }
  }
}

遠端端點

https://rapp-agent-builder.azurewebsites.net/finder/mcpstreamable-http

它能做什麼

工具清單

工具(5)

🟢 唯讀🟡 寫入🔴 刪除⚪ 未知
🟢find_agents(query, limit)

Searches the public RAPP Agent Registry (RAR, about 1,700 single-file agents) for agents that already do what the user wants, for example when they ask 'is there an AI tool for...' or want a ready-made automation instead of building one. Use it before building from scratch, or when the user asks whether an agent exists for a task.

輸入結構描述

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Plain words describing the task, e.g. 'summarize sales calls'"
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 10,
      "description": "How many results, default 5"
    }
  },
  "required": [
    "query"
  ],
  "additionalProperties": false
}
🟢get_agent_code(name)

Returns the full source of one agent from the registry, by its name (e.g. @bill/neuron_agent), so it can be read or adapted.

輸入結構描述

{
  "type": "object",
  "properties": {
    "name": {
      "type": "string",
      "description": "Registry name, e.g. @kody/memory_agent"
    }
  },
  "required": [
    "name"
  ],
  "additionalProperties": false
}
🟢use_agent_here(filename)

Use this right after an agent passes check_agent, or whenever the user wants to try an agent. Returns a short Python runner so you can run the agent in this chat with your own Python tool on the user's own data (pasted text, an uploaded spreadsheet, a list). Nothing to install. If Python is unavailable, apply the agent's logic yourself by reading its code. Ask the user for their real data, run the agent, and show the result in plain words.

輸入結構描述

{
  "type": "object",
  "properties": {
    "filename": {
      "type": "string",
      "description": "The agent file name, e.g. invoice_triage_agent.py"
    }
  },
  "additionalProperties": false
}
🟢how_to_run_agent(os, filename)

Optional, for later: how to keep an agent running on the user's own computer with the free RAPP Brainstem. Only offer this after the user has used the agent in the chat and wants to keep it.

輸入結構描述

{
  "type": "object",
  "properties": {
    "os": {
      "type": "string",
      "enum": [
        "mac",
        "windows",
        "linux"
      ],
      "description": "The user's operating system"
    },
    "filename": {
      "type": "string",
      "description": "The agent file name, if known"
    }
  },
  "additionalProperties": false
}
🟡request_service(request)

Use when the person wants something none of these tools can do and says yes to passing the request on. Records only the request text they agree to send (no name or contact). Ask before calling it.

輸入結構描述

{
  "type": "object",
  "properties": {
    "request": {
      "type": "string",
      "description": "What they want, in a sentence, as they agreed to send it"
    }
  },
  "required": [
    "request"
  ],
  "additionalProperties": false
}

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