Domain: Automation, Bot Development, Claude Collaboration Verification Level: Design Review Complete, Implementation Pending First Written: 2026-03-27 knowledge_store: kc-2026-03-27-004, kc-2026-03-27-005, kc-2026-03-27-008
Current Status (as of 2026-03-27)
The agent-memory vector DB holds 771 entries (Curated 189 / Structured 545 / Raw 37, across 15 projects). ProjectPocket leads with 385 entries.
The MCP server runs locally on Windows (~/session-memory/server) via uv, and tools are only active in Cowork sessions when the MCP is connected.
Problem: Unstable Access from Cowork
| Access Method | Result | Reason |
|---|---|---|
| agent-memory MCP tools (direct) | ✅ Works (when connected) | Always-running server approach |
| CLI wrapper (PowerShell → Python) | ❌ 60s timeout | Python startup cost + zombie processes + resource contention |
| knowledge_store.json file read | ✅ Works | Simple file read (no vector search) |
Core problem: The CLI approach goes through Python process startup → LanceDB load → Ollama call on every invocation. When Knowledge Curator is running, resource contention makes timeouts inevitable.
Proposed Solutions
A. Add FastMCP HTTP Endpoint (Recommended)
# Example: Search via HTTP bridge
$result = Invoke-RestMethod -Uri "http://localhost:8765/search" `
-Method Post -Body (@{query="..."} | ConvertTo-Json) `
-ContentType "application/json"
B. Separate Lightweight HTTP Wrapper Server
C. knowledge_store.json Keyword Fallback
$ks = Get-Content "~/infrastructure/knowledge_store.json" -Raw | ConvertFrom-Json
$ks.entries | Where-Object { $_.tags -contains "ProjectPocket" }
Note: Korean key duplication issues exist; exercise caution with ConvertFrom-Json.