Find Tools

Immutable Catalog Entry

This descriptor comes from the active versioned Portal catalog.

READleonardo-ai.find_tools

• Contract tier: agent_ready
• Risk: read
• Catalog version: leonardo-ai-2026.07.12.1
• Descriptor hash: cb1268cf8eb06aa22af072a41a9239f228fa2cd281d98a0d7e7807f82f8f75a9

Use this local read-only tool to rank Leonardo tools for a natural-language task using punctuation-normalized multi-token search. Filter by category or risk when useful; it does not contact Leonardo.Ai or inspect argument values.

Request Body

{
  "jsonrpc": "2.0",
  "id": "leonardo-ai-find-tools-example",
  "method": "tools/call",
  "params": {
    "name": "leonardo-ai.find_tools",
    "arguments": {
      "query": "query_example",
      "risk": "",
      "limit": 1,
      "category": "",
      "include_legacy": false
    }
  }
}

Arguments Schema

{
  "type": "object",
  "required": [
    "query"
  ],
  "properties": {
    "risk": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional risk filter: read, write, or destructive."
    },
    "limit": {
      "type": "integer",
      "default": 8,
      "description": "Maximum results to return; values are clamped from 1 through 25."
    },
    "query": {
      "type": "string",
      "description": "Natural-language task or capability phrase used for local ranked search."
    },
    "category": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional exact catalog category, such as generation, jobs, or models."
    },
    "include_legacy": {
      "type": "boolean",
      "default": false,
      "description": "Include legacy polling tools in addition to agent-ready descriptors."
    }
  },
  "additionalProperties": false
}

Output Schema

{
  "type": "object",
  "required": [
    "ok"
  ],
  "properties": {
    "ok": {
      "type": "boolean",
      "description": "Whether the navigation request succeeded."
    },
    "data": {
      "anyOf": [
        {
          "type": "object",
          "additionalProperties": true
        },
        {
          "type": "null"
        }
      ],
      "description": "Safe navigation data when the request succeeds."
    },
    "error": {
      "anyOf": [
        {
          "type": "object",
          "additionalProperties": true
        },
        {
          "type": "null"
        }
      ],
      "description": "Safe structured error when ok is false."
    },
    "manifest": {
      "anyOf": [
        {
          "type": "object",
          "additionalProperties": true
        },
        {
          "type": "null"
        }
      ],
      "description": "Complete ToolManifest when explicitly requested."
    }
  },
  "description": "Local Leonardo navigation result. ok=false is a semantic tool failure and must be handled as an error by the caller.",
  "additionalProperties": true
}

Code Examples

cURL

curl -X POST 'https://portal.madpanda3d.com/api/mcp' \
  -H 'Authorization: Bearer mad_live_***' \
  -H 'Content-Type: application/json' \
  -d '{
  "jsonrpc": "2.0",
  "id": "leonardo-ai-find-tools-example",
  "method": "tools/call",
  "params": {
    "name": "leonardo-ai.find_tools",
    "arguments": {
      "query": "query_example",
      "risk": "",
      "limit": 1,
      "category": "",
      "include_legacy": false
    }
  }
}'

Node.js

const response = await fetch('https://portal.madpanda3d.com/api/mcp', {
  method: 'POST',
  headers: {
    Authorization: 'Bearer mad_live_***',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
  "jsonrpc": "2.0",
  "id": "leonardo-ai-find-tools-example",
  "method": "tools/call",
  "params": {
    "name": "leonardo-ai.find_tools",
    "arguments": {
      "query": "query_example",
      "risk": "",
      "limit": 1,
      "category": "",
      "include_legacy": false
    }
  }
}),
});
const data = await response.json();
console.log(data);

Python

import requests

url = 'https://portal.madpanda3d.com/api/mcp'
headers = {
    'Authorization': 'Bearer mad_live_***',
    'Content-Type': 'application/json',
}
payload = {
  "jsonrpc": "2.0",
  "id": "leonardo-ai-find-tools-example",
  "method": "tools/call",
  "params": {
    "name": "leonardo-ai.find_tools",
    "arguments": {
      "query": "query_example",
      "risk": "",
      "limit": 1,
      "category": "",
      "include_legacy": false
    }
  }
}
response = requests.post(url, headers=headers, json=payload, timeout=60)
print(response.json())

Example Responses

200 Success

{
  "ok": true,
  "message": "Tool leonardo-ai.find_tools executed successfully.",
  "note": "Response shape varies by MCP tool. Inspect live responses for exact fields."
}

502 Upstream Error

{
  "ok": false,
  "classification": "upstream_http_error",
  "message": "Upstream MCP returned a non-success status."
}