brain-mcp

Instructions

Implementation Reference

• brain_mcp/server/tools_prosthetic.py:993-1082 (handler)

  The implementation of the `open_threads` tool, which retrieves and organizes open questions across domains from the database, or falls back to raw conversation data if necessary.

  def open_threads(limit_per_domain: int = 5, max_domains: int = 20) -> str:
      """
      Global inventory of ALL open questions across ALL domains.
      The 'unfinished business' dashboard.
      """
      db = get_summaries_db()
      if db:
          rows = db.execute("""
              SELECT domain_primary, open_questions, importance, thinking_stage
              FROM summaries
              WHERE domain_primary != '' AND domain_primary IS NOT NULL
          """).fetchall()
  
          if not rows:
              return "No data found."
  
          domain_data = {}
          for domain, oq_str, importance, stage in rows:
              if domain not in domain_data:
                  domain_data[domain] = {"questions": [], "count": 0, "bt": 0, "stage": stage}
              domain_data[domain]["count"] += 1
              if importance == "breakthrough":
                  domain_data[domain]["bt"] += 1
              for q in parse_json_field(oq_str):
                  if q and q.lower() != "none identified" and q not in domain_data[domain]["questions"]:
                      domain_data[domain]["questions"].append(q)
  
          active = [(d, v) for d, v in domain_data.items() if v["questions"]]
          active.sort(key=lambda x: (-x[1]["bt"], -len(x[1]["questions"])))
  
          total_q = sum(len(v["questions"]) for _, v in active)
          output = [f"## 🧵 Open Threads\n"]
          output.append(f"**{total_q} open questions** across **{len(active)} domains**\n")
  
          for domain, data in active[:max_domains]:
              bt = f" 💎×{data['bt']}" if data["bt"] else ""
              output.append(f"### {domain}{bt} ({len(data['questions'])} questions, {data['count']} convos)")
              for q in data["questions"][:limit_per_domain]:
                  output.append(f"  ❓ {q}")
              if len(data["questions"]) > limit_per_domain:
                  output.append(f"  _... and {len(data['questions'])-limit_per_domain} more_")
              output.append("")
  
          return "\n".join(output)
  
      # ── Fallback: user questions from raw conversations ──
      try:
          con = get_conversations()
      except FileNotFoundError:
          return "No conversation data found. Run the ingest pipeline first."
  
      rows = con.execute("""
          SELECT conversation_title, content, created
          FROM conversations
          WHERE role = 'user' AND has_question = 1
          ORDER BY created DESC
          LIMIT 50
      """).fetchall()
  
      if not rows:
          return "No questions found in conversations."
  
      # Group by conversation title
      topic_questions = {}
      for title, content, created in rows:
          key = title or "Untitled"
          if key not in topic_questions:
              topic_questions[key] = []
          topic_questions[key].append((content, created))
  
      total_q = sum(len(qs) for qs in topic_questions.values())
      output = [f"## 🧵 Open Threads (from raw conversations)\n"]
      output.append(f"**{total_q} recent questions** across **{len(topic_questions)} topics**\n")
  
      shown = 0
      for topic, questions in list(topic_questions.items()):
          if shown >= max_domains:
              break
          output.append(f"### {topic} ({len(questions)} questions)")
          for content, created in questions[:limit_per_domain]:
              snippet = (content or "")[:200].replace("\n", " ")
              output.append(f"  ❓ {snippet}")
          if len(questions) > limit_per_domain:
              output.append(f"  _... and {len(questions)-limit_per_domain} more_")
          output.append("")
          shown += 1
  
      output.append(_SUMMARIES_HINT)
      return "\n".join(output)