Feather DB as a Claude Context Engine: The MCP Remote Backend

What v0.14.0 ships

Feather DB v0.14.0 introduces feather-serve as an MCP server. Connect it to Claude Desktop or Claude Code and your Claude instance gets a persistent, semantic context engine — 14 MCP tools covering search, add, link, context_chain traversal, and more. Works against a local .feather file or a remote Feather Cloud instance via --api-url.

v0.15.1 went further: added real embedders via --embed-provider, making persona recall fully semantic. Claude can now search your memory store using natural language — no manual embedding calls, no preprocessing pipeline.

Why connect a database to an LLM via MCP?

Claude's context window resets every conversation. If you're building a coding assistant, a writing partner, a support agent, or any application where Claude should remember prior interactions, preferences, or knowledge — you need external memory.

The naive approach is to dump conversation history into the system prompt. That works for short sessions but breaks at scale: you hit token limits, costs multiply, and signal drowns in noise. A context engine retrieves only the relevant 5–10 memories, keeping prompts small and accurate.

MCP makes this connection first-class. Instead of wrapping memory calls in application code, Claude calls feather_search or feather_context_chain directly, mid-conversation, as a tool use.

Setup in under 5 minutes

pip install feather-db

# Start feather-serve with Gemini embeddings (semantic recall)
GOOGLE_API_KEY=your-key feather-serve mypersona.feather \
  --embed-provider gemini --dim 768 --port 8001

Then add to your claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "feather-persona": {
      "url": "http://localhost:8001/mcp"
    }
  }
}

Restart Claude Desktop. You'll see "feather-persona" appear in the MCP tools list. Claude can now call feather_search, feather_add, and 12 other tools against your local context store.

The 14 MCP tools

Real embedders: what --embed-provider changes

Without --embed-provider, feather_add requires a pre-embedded vector — you'd need to call an embedding API yourself before calling the tool. With --embed-provider gemini (or openai, voyage, cohere, ollama), feather-serve embeds text automatically inside the MCP call. You pass raw text; Feather handles the rest.

# Gemini — 768-dim, native Feather format, $0/1M tokens in free tier
GOOGLE_API_KEY=… feather-serve agent.feather \
  --embed-provider gemini --dim 768

# OpenAI — text-embedding-3-small, 1536-dim
OPENAI_API_KEY=… feather-serve agent.feather \
  --embed-provider openai --dim 1536

# Fully offline
feather-serve agent.feather \
  --embed-provider ollama --ollama-model nomic-embed-text --dim 1024

With real embedders, feather_search also accepts raw text queries — feather-serve embeds the query before searching. Claude doesn't need to know anything about embedding dimensions or models.

Connecting to Feather Cloud

For teams running Feather Cloud, the setup is identical except you point --api-url at your hosted instance:

feather-serve --api-url https://your-cloud.example.com \
  --embed-provider gemini --dim 768 --port 8001

This proxies MCP calls through feather-serve to the Cloud API, giving Claude the same 14 tools against a hosted, multi-tenant context store.

What this enables

A Claude instance with Feather DB MCP has persistent, evolving memory across conversations. In practice:

• Coding assistant: Claude remembers your codebase conventions, past debugging sessions, decisions made — without re-explaining every time
• Writing partner: Claude remembers your voice, past drafts, research notes, recurring characters or concepts
• Support agent: Claude recalls prior tickets, user preferences, known issues — contextualizing new requests against history
• Research assistant: Claude accumulates findings across sessions, links related papers, tracks open questions

The context_chain tool is particularly powerful here: it combines ANN search with graph traversal, surfacing not just semantically similar memories but their connected context — decisions that led to an outcome, papers that cite each other, tickets related by component.

Install

pip install feather-db  # includes feather-serve

GitHub: github.com/feather-store/feather · Docs: getfeather.store/docs/integrations