feat(brain): add brain_answer and brain_classify MCP tools

Adds two new LLM-backed MCP tools to the ingestion service:

- brain_answer(query): BM25 retrieval + LLM synthesis → answer + sources
- brain_classify(text): classifies doc into type/title/tags via LLM

Adds llm.Router for primary→fallback routing (berget.ai → iguana).
Wired via BRAIN_LLM_PRIMARY_URL/BRAIN_LLM_FALLBACK_URL env vars;
no-op when unset so existing deployments are unaffected.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

ingestion/internal/mcp/tools_answer.go

@@ -0,0 +1,114 @@
+package mcp
+
+import (
+	"context"
+	"encoding/json"
+	"fmt"
+	"strings"
+
+	"github.com/mathiasbq/hyperguild/ingestion/internal/search"
+)
+
+const (
+	answerSystemPrompt = `You are a knowledge assistant. Answer the question using ONLY the provided sources.
+Cite source file paths inline when referencing specific content.
+If the context does not contain enough information to answer, say so clearly.`
+
+	classifySystemPrompt = `Classify the document. Respond with JSON only, no markdown fences.
+{"type":"...","title":"...","tags":["..."]}
+Valid types: spec, plan, decision, note, wiki, log, code, unknown.`
+)
+
+type brainAnswerArgs struct {
+	Query string `json:"query"`
+}
+
+func (s *Server) brainAnswer(ctx context.Context, args json.RawMessage) (json.RawMessage, error) {
+	if s.answerLLM == nil {
+		return nil, fmt.Errorf("answer LLM not configured: set BRAIN_LLM_PRIMARY_URL")
+	}
+	var a brainAnswerArgs
+	if err := json.Unmarshal(args, &a); err != nil {
+		return nil, fmt.Errorf("parse args: %w", err)
+	}
+	if a.Query == "" {
+		return nil, fmt.Errorf("query is required")
+	}
+
+	results, err := search.Query(s.brainDir, a.Query, 10)
+	if err != nil {
+		return nil, fmt.Errorf("search: %w", err)
+	}
+	if len(results) == 0 {
+		return json.Marshal(map[string]any{
+			"answer":  "No relevant content found in brain.",
+			"sources": []string{},
+		})
+	}
+
+	var sb strings.Builder
+	sources := make([]string, 0, len(results))
+	for _, r := range results {
+		fmt.Fprintf(&sb, "<source path=%q>\n%s\n</source>\n\n", r.Path, r.Excerpt)
+		sources = append(sources, r.Path)
+	}
+
+	answer, err := s.answerLLM(ctx, answerSystemPrompt, sb.String()+"Question: "+a.Query)
+	if err != nil {
+		return nil, fmt.Errorf("llm: %w", err)
+	}
+
+	return json.Marshal(map[string]any{
+		"answer":  answer,
+		"sources": sources,
+	})
+}
+
+type brainClassifyArgs struct {
+	Text string `json:"text"`
+}
+
+type classifyResult struct {
+	Type  string   `json:"type"`
+	Title string   `json:"title"`
+	Tags  []string `json:"tags"`
+}
+
+func (s *Server) brainClassify(ctx context.Context, args json.RawMessage) (json.RawMessage, error) {
+	if s.answerLLM == nil {
+		return nil, fmt.Errorf("answer LLM not configured: set BRAIN_LLM_PRIMARY_URL")
+	}
+	var a brainClassifyArgs
+	if err := json.Unmarshal(args, &a); err != nil {
+		return nil, fmt.Errorf("parse args: %w", err)
+	}
+	if a.Text == "" {
+		return nil, fmt.Errorf("text is required")
+	}
+
+	text := a.Text
+	if len(text) > 3000 {
+		text = text[:3000]
+	}
+
+	raw, err := s.answerLLM(ctx, classifySystemPrompt, text)
+	if err != nil {
+		return nil, fmt.Errorf("llm: %w", err)
+	}
+
+	// Strip markdown fences if model adds them despite the instruction.
+	raw = strings.TrimSpace(raw)
+	raw = strings.TrimPrefix(raw, "```json")
+	raw = strings.TrimPrefix(raw, "```")
+	raw = strings.TrimSuffix(raw, "```")
+	raw = strings.TrimSpace(raw)
+
+	var cr classifyResult
+	if err := json.Unmarshal([]byte(raw), &cr); err != nil {
+		return nil, fmt.Errorf("parse classify response %q: %w", raw, err)
+	}
+	if cr.Tags == nil {
+		cr.Tags = []string{}
+	}
+	return json.Marshal(cr)
+}