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Just want to wire up an MCP client quickly? See For AI Agents for a 2-minute quickstart with all the major IDE configs in one place. This page is the deep-dive reference.
The Reflect MCP server exposes two tools — retrieve_memories and create_memory — over the Model Context Protocol. Connect it to any MCP-capable agent or IDE and your AI assistant will automatically query past lessons before hard tasks and record new ones after each run. MCP endpoint: https://api.starlight-search.com/mcp
Auth: Authorization: Bearer <your-api-key>
Transport: Streamable HTTP
Get your API key from the Reflect console.

Claude Code

Add the server to your project or global config:
Or add it manually to .claude/settings.json (project) or ~/.claude/settings.json (global):
Verify it loaded:

Cursor

Open Settings → MCP (or ~/.cursor/mcp.json) and add:
Restart Cursor. The tools appear in Agent mode automatically.

Windsurf

Open Settings → Cascade → MCP Servers and add a new server:

Cline / Continue / other MCP clients

Any client that supports streamable HTTP transport uses the same config pattern:

Per-project scoping with X-Project-Id

By default the server uses the project ID configured on your API key. To scope memories to a specific project per-request, pass the X-Project-Id header:

Available tools

retrieve_memories

Search the memory store for lessons from prior tasks. Call this before starting non-trivial work. Returns a list of memories with id, task, reflection, q_value, similarity, score, and success. Save the memory_ids — you’ll pass them to create_memory.

create_memory

Persist a memory the agent writes itself. You author the reflection (summary + guidance) from what you just did and submit it with a pass/fail result — Reflect stores it directly, with no trajectory and no background model. Call this after the user confirms success or gives corrective feedback.

Verify the connection

The health endpoint requires no auth: