rapp-agent-builder

Turn an idea or meeting transcript into a working single-file AI agent, check it, use it right away.

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
98%
Vollständigkeit des Schemas
89%
Qualität der Benennung
90%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~1,139Tokens (Tool-Definitionen)
~606 BTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (0.89% von 128k Kontext)

Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.

Installieren

Installation mit einem Klick

Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:

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

Remote-Endpunkte

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

Was es kann

Tool-Inventar

Tools (8)

🟢 Nur lesen🟡 Schreiben🔴 Löschen⚪ Unbekannt
🟢get_agent_template

Use this when the user wants to build an AI agent, assistant, bot or automation from an idea, a repetitive task, a process description, or a meeting transcript (for example: automate invoices, triage support tickets, summarize meetings, follow up with leads). Returns the official single-file RAPP agent template and its rules. Fill it in yourself from what the user described, then call check_agent on the finished file before showing it to the user. Then call use_agent_here so the user can use it right away in this chat.

Eingabe-Schema

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢check_agent(filename, code)

Checks a finished RAPP agent file against the template rules (manifest, naming, class, perform method, no hardcoded secrets). Call it on every agent you write, fix anything it reports, and call it again until it passes.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "filename": {
      "type": "string",
      "description": "File name, must end with _agent.py, e.g. invoice_triage_agent.py"
    },
    "code": {
      "type": "string",
      "description": "The full Python source of the agent file"
    }
  },
  "required": [
    "filename",
    "code"
  ],
  "additionalProperties": false
}
🟢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.

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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
}
🟢share_agent(filename, code)

Use only when the user says they want to share an agent they built. Checks the agent and scans it for personal details (emails, phone numbers, secrets), then explains how to publish it to the free public RAPP Agent Registry under their own GitHub account. Nothing is published by this tool.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "filename": {
      "type": "string",
      "description": "Agent file name, ending in _agent.py"
    },
    "code": {
      "type": "string",
      "description": "The full Python source of the agent"
    }
  },
  "required": [
    "filename",
    "code"
  ],
  "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.

Eingabe-Schema

{
  "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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