rar-agent-finder
Find a free, ready-made AI agent for a task in the public RAPP Agent Registry.
Sollte ich dies verwenden
Qualität und Sicherheit
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
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": {
"rar-agent-finder": {
"url": "https://rapp-agent-builder.azurewebsites.net/finder/mcp"
}
}
}Remote-Endpunkte
https://rapp-agent-builder.azurewebsites.net/finder/mcpstreamable-httpWas es kann
Tool-Inventar
Tools (5)
🟢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
}🟡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
}Community
Nachweis