Galdr
Valhalla
Backend MCP server for teams
42 server-backed MCP tools: semantic memory search, Oracle DB, MediaWiki, platform crawling, video analysis, and cross-machine session continuity.
Quick Start
Features
What you get
42 MCP server tools
8 categories: Memory, Vault, Install, Crawling, Oracle, MediaWiki, Video, Utility.
Semantic memory search
pgvector-backed embeddings. Search across all session summaries and project captures.
memory_capture_session
AI self-reports session summary to persistent memory after every conversation.
memory_context
Token-budgeted context block injected at session start — cross-machine memory.
Oracle DB query/execute
Full Oracle DB read/write access via MCP tools. Source + target DB separation.
MediaWiki integration
Get, create, update wiki pages. Full-text search. Integrate team knowledge bases.
Platform doc crawling
Weekly-refreshed docs for Cursor, Claude, Gemini — stored in pgvector for semantic search.
galdr_install tool
Initialize galdr in any project remotely via MCP tool call.
galdr_health_report
Per-subsystem health score 0-100. Stale claims, blocked tasks, Firecrawl status.
Video analysis pipeline
Frame extraction + transcript + vision analysis → vault notes.
Agents
Specialized agents
g-agnt-project
g-agnt-project.md
Manages PROJECT.md, goals, and project identity
g-agnt-task-manager
g-agnt-task-manager.md
Owns TASKS.md and individual task files
g-agnt-code-reviewer
g-agnt-code-reviewer.md
Adversarial code review — separate from implementing agent
g-agnt-qa-engineer
g-agnt-qa-engineer.md
Bug filing, QA workflow, quality metrics
g-agnt-verifier
g-agnt-verifier.md
Independent verification of [🔍] items
g-agnt-infrastructure
g-agnt-infrastructure.md
DevOps, CI/CD, Docker, cloud infra
g-agnt-ideas-goals
g-agnt-ideas-goals.md
IDEA_BOARD → task promotion + goal tracking
g-agnt-test
g-agnt-test.md
L1/L2/L3 test plan creation and execution
g-agnt-pcac-coordinator
g-agnt-pcac-coordinator.md
Orchestrates cross-project PCAC operations
Workflows
How it works
Typical workflow
- 1
Deploy Docker Stack
docker compose up -d → Postgres, pgvector, galdr API, crawl4ai
- 2
Configure MCP
Add galdr MCP server to Cursor/Claude MCP config
- 3
Capture Sessions
memory_capture_session → session summaries persist to pgvector
- 4
Search Memory
memory_search → semantic query across all stored sessions
- 5
Inject Context
memory_context → token-budgeted block injected at next session start
Valhalla Docker Stack
flowchart TD IDE([Cursor / Claude Code\nGemini / Codex]) -->|MCP protocol| G([galdr MCP server\nDocker]) G --> DB[(Postgres\n+ pgvector)] G --> C([crawl4ai\nplatform docs]) G --> W([MediaWiki\nteam knowledge]) G --> O[(Oracle DB\nread / write)] G --> V([Video analysis\nyt-dlp + vision]) style IDE fill:#1e293b,stroke:#60a5fa,color:#60a5fa style G fill:#1e293b,stroke:#c9922a,color:#c9922a style DB fill:#1e293b,stroke:#a78bfa,color:#a78bfa style C fill:#1e293b,stroke:#6ee7b7,color:#6ee7b7 style W fill:#1e293b,stroke:#6ee7b7,color:#6ee7b7 style O fill:#1e293b,stroke:#f87171,color:#f87171 style V fill:#1e293b,stroke:#94a3b8,color:#94a3b8
Mermaid diagram — paste into any Mermaid renderer to visualize