Add Generative Answers

Add SearchAndSummarizeContent nodes to generate responses grounded in the agent's knowledge sources.

Important: Do You Even Need a Topic?

When knowledge sources are added to the agent (via /add-knowledge), the AI can directly search them without any topic if it recognizes a QnA-style query. A dedicated topic with SearchAndSummarizeContent is useful when:

• You want to restrict the search to a subset of knowledge sources (not all of them)
• You want to control the flow around the answer (e.g., follow-up questions, formatting, adaptive cards)
• You want to process the response before showing it (e.g., extract content, combine with other data)
• You want to use a specific input other than the user's last message

If the user just wants the agent to answer questions from its knowledge, adding the knowledge source may be enough.

Instructions

1. Auto-discover the agent directory:

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   Glob: **/agent.mcs.yml

   NEVER hardcode an agent name.

2. Determine the approach based on what the user needs:

   • Add to existing topic: Read the target topic and insert a SearchAndSummarizeContent node
   • Create new search topic: Generate a complete topic with the search pattern

3. Look up the schema for both nodes:

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   node ${CLAUDE_SKILL_DIR}/../../scripts/schema-lookup.bundle.js resolve CreateSearchQuery
   node ${CLAUDE_SKILL_DIR}/../../scripts/schema-lookup.bundle.js resolve SearchAndSummarizeContent

4. Read settings.mcs.yml to check if GenerativeActionsEnabled: true. This determines the best pattern:

   • GenerativeActionsEnabled: true → prefer Pattern 2 (Orchestrator): use topic inputs/outputs and let the orchestrator handle the response. This is the best approach for generative-orchestrated agents.
   • GenerativeActionsEnabled: false (or not set) → use Pattern 1 (Direct Response): autoSend: false + manual SendActivity, or Pattern 3 (Fallback Search) for a simple all-knowledge fallback.
   • Verbatim/exact content needed → use Pattern 4 (Precision Search): SearchKnowledgeSources + CreateSearchQuery for raw results without AI summarization (insurance policies, HR docs, legal text).

5. Ask the user to clarify the behavior (if not already clear from their request):

   • Should it search all knowledge sources or only specific ones?
   • Should general model knowledge also be used, or only the configured knowledge sources?
   • Should the response be sent automatically to chat, or processed first (e.g., custom formatting, adaptive card, combined with other data)?

6. Always precede SearchAndSummarizeContent with CreateSearchQuery to preserve conversational context. Never pass =System.Activity.Text directly to SearchAndSummarizeContent — the raw last message may lack context (e.g., "tell me more about that"). CreateSearchQuery rewrites the input into an optimized search query. Access the result via Topic.<ResultVar>.SearchQuery.

7. Generate unique IDs for all nodes (format: <nodeType>_<6-8 random alphanumeric>).

8. Build the YAML using the appropriate pattern. For full YAML examples, see patterns.md. For the complete property reference table, see property-reference.md.

SearchAndSummarizeContent vs AnswerQuestionWithAI

Use SearchAndSummarizeContent for the vast majority of cases (what people call "generative answers"). Use SearchKnowledgeSources when you need raw, unsummarized results for precision scenarios (pair with CreateSearchQuery for better search accuracy). Use AnswerQuestionWithAI only when you explicitly want the model to respond without consulting knowledge sources.

Knowledge Source References

When using knowledgeSources to restrict the search to specific sources:

1. The knowledge source must already exist in the agent (add it first with /add-knowledge)
2. Find the knowledge source filename in the agent's knowledge/ directory
3. Reference it without the .mcs.yml extension

Example: if the file is cre3c_agent.topic.MyDocs_abc123.mcs.yml, the reference is:

knowledgeSources:
  kind: SearchSpecificKnowledgeSources
  knowledgeSources:
    - cre3c_agent.topic.MyDocs_abc123