vectorize

name	vectorize
description	Runs semantic RAG and vector search over indexed knowledge via Vectorize MCP tools discovered at runtime. Use for memory retrieval, knowledge-base lookup, and evidence-backed answers from chunks.

Vectorize

Purpose

Retrieve relevant passages from vector-indexed content: phrase queries clearly, treat chunks as evidence, summarize with brief citations—using tool names from the Vectorize MCP server (list_tools).

When to Use

• Semantic search over a knowledge base or indexed corpus.
• “What do we know about …” questions needing retrieved context.
• Finding notes or docs by meaning, not exact filename.

When NOT to Use

• Authoritative Syncolab platform docs → syncolab-living-docs (SKS).
• Tenant long-term agent memory (CML REST/hydration) → syncolab-cognitive-memory.
• Fabricating answers without running a query tool.

Expected Outcome

• One focused natural-language query first; refine only if results are weak.
• Response summarizes chunks; does not dump raw text walls.
• Tool names and parameters from live MCP schema.

Inputs to Gather

• Clear natural-language query (avoid overly short keywords).
• Optional filters if the MCP schema exposes them (collection, tenant, etc.).

Workflow

1. Discover Vectorize tools via MCP list_tools; read schema before call.
2. Run a single well-phrased query.
3. If results are off-topic, refine query once with more context.
4. Synthesize answer with short citations to chunk sources/ids when present.

Domain rules

1. Phrase queries clearly in natural language.
2. Chunks are evidence—summarize and cite, do not paste entire indexes.
3. One focused query first before iterating.

Main tools

• Vectorize MCP query/retrieve tools (names from list_tools at runtime).

Examples

Q4 launch: Query "Q4 product launch"; return concise summary with chunk references.

API auth flow: Query "API authentication flow"; present matching passages briefly.

Tool Availability Rules

Access	Behavior
Full MCP access	Query and summarize results.
Read-only	Query only.
No Vectorize MCP	Do not invent retrieved text.

Related tool sets

• openai

Review / Decision / Execution Criteria

• Prefer precision over dumping all chunks.
• Distinguish low-confidence empty results from “no data.”

Output Format

1. Query used.
2. Summary with citations.
3. Gaps or low relevance.
4. SKS/CML skill if user needs platform canonical docs or tenant memory.

Quality Bar

• Grounded answers only in retrieved chunks.
• No hallucinated citations.

Safety and Boundaries

• Do not leak sensitive chunk content beyond user need.
• No secrets in queries.

Escalation / Dispatch Rules

• Platform truth → syncolab-living-docs.
• Agent memory layer → syncolab-cognitive-memory.

References

• Legacy: skills/old_skills.json (vectorize).
• skills/skill.instruction.md, skills/meta.instructions.md