LLM Agent Integration

{
  "name": "search_knowledge",
  "description": "Search the user's personal knowledge base (their imported articles, videos, PDFs) by natural-language query. Returns the most relevant section excerpts.",
  "input_schema": {
    "type": "object",
    "properties": {
      "query": { "type": "string", "description": "Natural-language search query" },
      "limit": { "type": "integer", "default": 5, "maximum": 25 }
    },
    "required": ["query"]
  }
}

const r = await fetch('https://skilldiscs.com/api/v1/search', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${process.env.SKILLDISCS_API_KEY}`,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify(input),
})
return await r.json()

const { results } = await r.json()
const context = results.map(hit => ({
  title: hit.disk_title,
  text: hit.section_text ?? hit.snippet,
  key_points: hit.key_points ?? [],
  redacted: hit.redacted,
}))

You have access to the user's personal knowledge base via three tools:
- search_knowledge(query, limit) — semantic search across owned Disks
- read_disk(id) — metadata, key points, and conditional text for one Disk
- list_disks(scope?, category?, tag?) — browse the library

Always cite the Disk title when referencing retrieved content.
Prefer search_knowledge over read_disk unless the user names a specific Disk.
If a result is redacted, treat snippet/key_points as context and do not imply access to the full source.