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.

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

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

Domain rules

Phrase queries clearly in natural language.
Chunks are evidence—summarize and cite, do not paste entire indexes.
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

Query used.
Summary with citations.
Gaps or low relevance.
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