Memora vs Facthouse — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Memora vs Facthouse
In-depth architectural comparison of the Memora and Facthouse MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Memora
Knowledge & Memory · Local stdio
Quality: 56/100 (Good) | Auth: API Key required
Facthouse
Knowledge & Memory · Local stdio
Quality: 52/100 (Good) | Auth: No auth required
Verdict Summary: Choose Memora if you need specialized Knowledge & Memory tools running via a local process. Choose Facthouse if your workspace requires Knowledge & Memory integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Memora when:
You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: API Key required (BYOK (Pay Provider Direct)).
You have access to required keys: MEMORA_DB_PATH, MEMORA_STORAGE_URI, CLOUDFLARE_API_TOKEN, AWS_PROFILE, AWS_ENDPOINT_URL, MEMORA_CLOUD_ENCRYPT, MEMORA_ALLOW_ANY_TAG, MEMORA_GRAPH_PORT.
Primary tools included: Persistent storage with SQLite or cloud sync (S3, R2, D1), Hierarchical memory organization with sections and subsections, Semantic search using TF-IDF, sentence-transformers, and OpenAI embeddings.
Memora is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, Facthouse belongs to Knowledge & Memory using local stdio subprocess. Select Memora when you need capabilities focused on knowledge & memory and Facthouse when you require tools for knowledge & memory.
Working briefing (the same markdown as `memory://briefing`) plus facts captured in this session. Call at the start of every conversation if the client does not load resources.
get_entity
Everything known about any named subject — person, organisation, project, place, product — and how it connects. When several rows share the name under different types, facts from all of them come back. Hyphens, underscores, and stray punctuation count as the same letters only when that does not joi…
get_context
Everything relevant to a topic (search + entity traversal)
search_knowledge
Hybrid search across integrated knowledge
capture_fact
Store a fact. On a copy store this is a correction for something extraction missed; on a store with empty `sources` it is how facts get in. The description the assistant sees is generated from that same rule.
consolidate
Integrate pending facts into long-term knowledge. Extracts entities, resolves duplicates, detects contradictions, builds the knowledge graph.