rar-agent-finder
Find a free, ready-made AI agent for a task in the public RAPP Agent Registry.
Should I use this
Quality & Safety
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"rar-agent-finder": {
"url": "https://rapp-agent-builder.azurewebsites.net/finder/mcp"
}
}
}Remote endpoints
https://rapp-agent-builder.azurewebsites.net/finder/mcpstreamable-httpWhat it can do
Tool inventory
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.
Input 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.
Input 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.
Input 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.
Input 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.
Input 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
Evidence